From 9afecf50749301451934e9b6956e7ebc77365f77 Mon Sep 17 00:00:00 2001
From: Daniel Palenicek <daniel.palenicek@tu-darmstadt.de>
Date: Fri, 3 May 2024 11:50:18 +0200
Subject: [PATCH 01/32] Implemented CrossQ

---
 README.md                            |   1 +
 docs/images/crossQ_performance.png   | Bin 0 -> 266220 bytes
 docs/index.rst                       |   1 +
 docs/modules/crossq.rst              |  99 ++++++
 sb3_contrib/__init__.py              |   2 +
 sb3_contrib/common/network_layers.py |  98 ++++++
 sb3_contrib/crossq/__init__.py       |   4 +
 sb3_contrib/crossq/crossq.py         | 330 ++++++++++++++++++++
 sb3_contrib/crossq/policies.py       | 450 +++++++++++++++++++++++++++
 tests/test_run.py                    |  12 +-
 10 files changed, 996 insertions(+), 1 deletion(-)
 create mode 100644 docs/images/crossQ_performance.png
 create mode 100644 docs/modules/crossq.rst
 create mode 100644 sb3_contrib/common/network_layers.py
 create mode 100644 sb3_contrib/crossq/__init__.py
 create mode 100644 sb3_contrib/crossq/crossq.py
 create mode 100644 sb3_contrib/crossq/policies.py

diff --git a/README.md b/README.md
index f54ae854..5b0503ba 100644
--- a/README.md
+++ b/README.md
@@ -31,6 +31,7 @@ See documentation for the full list of included features.
 - [PPO with recurrent policy (RecurrentPPO aka PPO LSTM)](https://ppo-details.cleanrl.dev//2021/11/05/ppo-implementation-details/)
 - [Truncated Quantile Critics (TQC)](https://arxiv.org/abs/2005.04269)
 - [Trust Region Policy Optimization (TRPO)](https://arxiv.org/abs/1502.05477)
+- [Batch Normalization in Deep Reinforcement Learning (CrossQ)](https://openreview.net/forum?id=PczQtTsTIX)
 
 **Gym Wrappers**:
 - [Time Feature Wrapper](https://arxiv.org/abs/1712.00378)
diff --git a/docs/images/crossQ_performance.png b/docs/images/crossQ_performance.png
new file mode 100644
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diff --git a/docs/index.rst b/docs/index.rst
index 5e322652..3e7c7a4e 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -32,6 +32,7 @@ RL Baselines3 Zoo also offers a simple interface to train, evaluate agents and d
   :caption: RL Algorithms
 
   modules/ars
+  modules/crossq
   modules/ppo_mask
   modules/ppo_recurrent
   modules/qrdqn
diff --git a/docs/modules/crossq.rst b/docs/modules/crossq.rst
new file mode 100644
index 00000000..e81aaa25
--- /dev/null
+++ b/docs/modules/crossq.rst
@@ -0,0 +1,99 @@
+.. _crossq:
+
+.. automodule:: sb3_contrib.crossq
+
+
+CrossQ
+======
+
+Implementation of CrossQ proposed in:
+
+`Bhatt A.* & Palenicek D.* et al. Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity. ICLR 2024.`
+
+CrossQ is a simple and efficient algorithm that uses batch normalization to improve the sample efficiency of off-policy deep reinforcement learning algorithms.
+It is based on the idea of carefully introducing batch normalization layers in the critic network and dropping target networks.
+This yield a simpler and more sample-efficient algorithm without requiring high update-to-data ratios.
+
+.. rubric:: Available Policies
+
+.. autosummary::
+    :nosignatures:
+
+    MlpPolicy
+
+
+Notes
+-----
+
+- Original paper: https://openreview.net/pdf?id=PczQtTsTIX
+- Original Implementation: https://github.com/adityab/CrossQ
+
+
+Can I use?
+----------
+
+-  Recurrent policies: ❌
+-  Multi processing: ✔️
+-  Gym spaces:
+
+
+============= ====== ===========
+Space         Action Observation
+============= ====== ===========
+Discrete      ❌      ✔️
+Box           ✔️      ✔️
+MultiDiscrete ❌      ✔️
+MultiBinary   ❌      ✔️
+Dict          ❌      ✔️
+============= ====== ===========
+
+
+Example
+-------
+
+.. code-block:: python
+
+  import gymnasium as gym
+  import numpy as np
+
+  from sb3_contrib import CrossQ
+
+  model = CrossQ("MlpPolicy", "Walker2d-v4")
+  model.learn(total_timesteps=1_000_000)
+  model.save("crossq_walker")
+
+
+Results
+-------
+
+Performance evaluation of CrossQ on six MuJoCo environments.
+Compared to results from the original paper as well as a version from SBX.
+
+.. image:: ../images/crossQ_performance.png
+
+Comments
+--------
+
+This implementation is based on SB3 SAC implementation.
+
+
+Parameters
+----------
+
+.. autoclass:: CrossQ
+  :members:
+  :inherited-members:
+
+.. _crossq_policies:
+
+CrossQ Policies
+---------------
+
+.. autoclass:: MlpPolicy
+  :members:
+  :inherited-members:
+
+.. autoclass:: sb3_contrib.crossq.policies.CrossQPolicy
+  :members:
+  :noindex:
+
diff --git a/sb3_contrib/__init__.py b/sb3_contrib/__init__.py
index 3fbd28d8..5a5f6243 100644
--- a/sb3_contrib/__init__.py
+++ b/sb3_contrib/__init__.py
@@ -1,6 +1,7 @@
 import os
 
 from sb3_contrib.ars import ARS
+from sb3_contrib.crossq import CrossQ
 from sb3_contrib.ppo_mask import MaskablePPO
 from sb3_contrib.ppo_recurrent import RecurrentPPO
 from sb3_contrib.qrdqn import QRDQN
@@ -14,6 +15,7 @@
 
 __all__ = [
     "ARS",
+    "CrossQ",
     "MaskablePPO",
     "RecurrentPPO",
     "QRDQN",
diff --git a/sb3_contrib/common/network_layers.py b/sb3_contrib/common/network_layers.py
new file mode 100644
index 00000000..524c5274
--- /dev/null
+++ b/sb3_contrib/common/network_layers.py
@@ -0,0 +1,98 @@
+import torch
+
+__all__ = ["BatchRenorm1d"]
+
+
+import torch
+import torch.nn as nn
+
+
+class BatchRenorm(torch.jit.ScriptModule):
+    """
+    BatchRenorm Module (https://arxiv.org/abs/1702.03275).
+    Adapted from flax.linen.normalization.BatchNorm
+
+    BatchRenorm is an improved version of vanilla BatchNorm. Contrary to BatchNorm,
+    BatchRenorm uses the running statistics for normalizing the batches after a warmup phase.
+    This makes it less prone to suffer from "outlier" batches that can happen
+    during very long training runs and, therefore, is more robust during long training runs.
+
+    During the warmup phase, it behaves exactly like a BatchNorm layer.
+
+    Args:
+        num_features: Number of features in the input tensor.
+        eps: A value added to the variance for numerical stability.
+        momentum: The value used for the running_mean and running_var computation.
+        affine: A boolean value that when set to True, this module has learnable
+            affine parameters. Default: True
+    """
+
+    def __init__(
+        self,
+        num_features: int,
+        eps: float = 0.001,
+        momentum: float = 0.01,
+        affine: bool = True,
+    ):
+        super().__init__()
+        self.register_buffer("running_mean", torch.zeros(num_features, dtype=torch.float))
+        self.register_buffer("running_var", torch.ones(num_features, dtype=torch.float))
+        self.register_buffer("num_batches_tracked", torch.tensor(0, dtype=torch.long))
+        self.scale = torch.nn.Parameter(torch.ones(num_features, dtype=torch.float))
+        self.bias = torch.nn.Parameter(torch.zeros(num_features, dtype=torch.float))
+
+        self.affine = affine
+        self.eps = eps
+        self.step = 0
+        self.momentum = momentum
+        self.rmax = 3.0
+        self.dmax = 5.0
+
+    def _check_input_dim(self, x: torch.Tensor) -> None:
+        raise NotImplementedError()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+
+        if self.training:
+            batch_mean = x.mean(0)
+            batch_var = x.var(0)
+            batch_std = (batch_var + self.eps).sqrt()
+
+            # Use batch statistics during initial warm up phase.
+            if self.num_batches_tracked > 100_000:
+
+                running_std = (self.running_var + self.eps).sqrt()
+                running_mean = self.running_mean
+
+                r = (batch_std / running_std).detach()
+                r = r.clamp(1 / self.rmax, self.rmax)
+                d = ((batch_mean - running_mean) / running_std).detach()
+                d = d.clamp(-self.dmax, self.dmax)
+
+                m = batch_mean - d * batch_var.sqrt() / r
+                v = batch_var / (r**2)
+
+            else:
+                m, v = batch_mean, batch_var
+
+            # Update Running Statistics
+            self.running_mean += self.momentum * (batch_mean.detach() - self.running_mean)
+            self.running_var += self.momentum * (batch_var.detach() - self.running_var)
+            self.num_batches_tracked += 1
+
+        else:
+            m, v = self.running_mean, self.running_var
+
+        # Normalize
+        x = (x - m[None]) / (v[None] + self.eps).sqrt()
+
+        if self.affine:
+            x = self.scale * x + self.bias
+
+        return x
+
+
+class BatchRenorm1d(BatchRenorm):
+    def _check_input_dim(self, x: torch.Tensor) -> None:
+        if x.dim() == 1:
+            raise ValueError("expected 2D or 3D input (got {x.dim()}D input)")
diff --git a/sb3_contrib/crossq/__init__.py b/sb3_contrib/crossq/__init__.py
new file mode 100644
index 00000000..9a1e4561
--- /dev/null
+++ b/sb3_contrib/crossq/__init__.py
@@ -0,0 +1,4 @@
+from sb3_contrib.crossq.crossq import CrossQ
+from sb3_contrib.crossq.policies import MlpPolicy
+
+__all__ = ["CrossQ", "MlpPolicy"]
diff --git a/sb3_contrib/crossq/crossq.py b/sb3_contrib/crossq/crossq.py
new file mode 100644
index 00000000..de739af5
--- /dev/null
+++ b/sb3_contrib/crossq/crossq.py
@@ -0,0 +1,330 @@
+from typing import Any, ClassVar, Dict, List, Optional, Tuple, Type, TypeVar, Union
+
+import numpy as np
+import torch as th
+from gymnasium import spaces
+from stable_baselines3.common.buffers import ReplayBuffer
+from stable_baselines3.common.noise import ActionNoise
+from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
+from stable_baselines3.common.policies import BasePolicy, ContinuousCritic
+from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
+from stable_baselines3.common.utils import get_parameters_by_name, polyak_update
+from torch.nn import functional as F
+
+from sb3_contrib.crossq.policies import Actor, CrossQPolicy, CrossQCritic, MlpPolicy
+
+SelfCrossQ = TypeVar("SelfCrossQ", bound="CrossQ")
+
+
+class CrossQ(OffPolicyAlgorithm):
+    """
+    Implementation of the CrossQ algorithm.
+    Paper: https://openreview.net/pdf?id=PczQtTsTIX
+
+    :param policy: The policy model to use (MlpPolicy)
+    :param env: The environment to learn from (if registered in Gym, can be str)
+    :param learning_rate: learning rate for adam optimizer,
+        the same learning rate will be used for all networks (Q-Values, Actor and Value function)
+        it can be a function of the current progress remaining (from 1 to 0)
+    :param buffer_size: size of the replay buffer
+    :param learning_starts: how many steps of the model to collect transitions for before learning starts
+    :param batch_size: Minibatch size for each gradient update
+    :param gamma: the discount factor
+    :param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit
+        like ``(5, "step")`` or ``(2, "episode")``.
+    :param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``)
+        Set to ``-1`` means to do as many gradient steps as steps done in the environment
+        during the rollout.
+    :param action_noise: the action noise type (None by default), this can help
+        for hard exploration problem. Cf common.noise for the different action noise type.
+    :param replay_buffer_class: Replay buffer class to use (for instance ``HerReplayBuffer``).
+        If ``None``, it will be automatically selected.
+    :param replay_buffer_kwargs: Keyword arguments to pass to the replay buffer on creation.
+    :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
+        at a cost of more complexity.
+        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
+    :param ent_coef: Entropy regularization coefficient. (Equivalent to
+        inverse of reward scale in the original SAC paper.)  Controlling exploration/exploitation trade-off.
+        Set it to 'auto' to learn it automatically (and 'auto_0.1' for using 0.1 as initial value)
+    :param target_entropy: target entropy when learning ``ent_coef`` (``ent_coef = 'auto'``)
+    :param use_sde: Whether to use generalized State Dependent Exploration (gSDE)
+        instead of action noise exploration (default: False)
+    :param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
+        Default: -1 (only sample at the beginning of the rollout)
+    :param use_sde_at_warmup: Whether to use gSDE instead of uniform sampling
+        during the warm up phase (before learning starts)
+    :param stats_window_size: Window size for the rollout logging, specifying the number of episodes to average
+        the reported success rate, mean episode length, and mean reward over
+    :param tensorboard_log: the log location for tensorboard (if None, no logging)
+    :param policy_kwargs: additional arguments to be passed to the policy on creation
+    :param verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for
+        debug messages
+    :param seed: Seed for the pseudo random generators
+    :param device: Device (cpu, cuda, ...) on which the code should be run.
+        Setting it to auto, the code will be run on the GPU if possible.
+    :param _init_setup_model: Whether or not to build the network at the creation of the instance
+    """
+
+    policy_aliases: ClassVar[Dict[str, Type[BasePolicy]]] = {
+        "MlpPolicy": MlpPolicy,
+    }
+    policy: CrossQPolicy
+    actor: Actor
+    critic: CrossQCritic
+
+    def __init__(
+        self,
+        policy: Union[str, Type[CrossQPolicy]],
+        env: Union[GymEnv, str],
+        learning_rate: Union[float, Schedule] = 1e-3,
+        buffer_size: int = 1_000_000,  # 1e6
+        learning_starts: int = 100,
+        batch_size: int = 256,
+        gamma: float = 0.99,
+        train_freq: Union[int, Tuple[int, str]] = 1,
+        gradient_steps: int = 1,
+        action_noise: Optional[ActionNoise] = None,
+        replay_buffer_class: Optional[Type[ReplayBuffer]] = None,
+        replay_buffer_kwargs: Optional[Dict[str, Any]] = None,
+        optimize_memory_usage: bool = False,
+        ent_coef: Union[str, float] = "auto",
+        target_entropy: Union[str, float] = "auto",
+        use_sde: bool = False,
+        sde_sample_freq: int = -1,
+        use_sde_at_warmup: bool = False,
+        stats_window_size: int = 100,
+        tensorboard_log: Optional[str] = None,
+        policy_kwargs: Optional[Dict[str, Any]] = None,
+        verbose: int = 0,
+        seed: Optional[int] = None,
+        device: Union[th.device, str] = "auto",
+        _init_setup_model: bool = True,
+    ):
+        super().__init__(
+            policy,
+            env,
+            learning_rate,
+            buffer_size,
+            learning_starts,
+            batch_size,
+            1.0,
+            gamma,
+            train_freq,
+            gradient_steps,
+            action_noise,
+            replay_buffer_class=replay_buffer_class,
+            replay_buffer_kwargs=replay_buffer_kwargs,
+            policy_kwargs=policy_kwargs,
+            stats_window_size=stats_window_size,
+            tensorboard_log=tensorboard_log,
+            verbose=verbose,
+            device=device,
+            seed=seed,
+            use_sde=use_sde,
+            sde_sample_freq=sde_sample_freq,
+            use_sde_at_warmup=use_sde_at_warmup,
+            optimize_memory_usage=optimize_memory_usage,
+            supported_action_spaces=(spaces.Box,),
+            support_multi_env=True,
+        )
+
+        self.target_entropy = target_entropy
+        self.log_ent_coef = None  # type: Optional[th.Tensor]
+        # Entropy coefficient / Entropy temperature
+        # Inverse of the reward scale
+        self.ent_coef = ent_coef
+        self.ent_coef_optimizer: Optional[th.optim.Adam] = None
+
+        if _init_setup_model:
+            self._setup_model()
+
+    def _setup_model(self) -> None:
+        super()._setup_model()
+        self._create_aliases()
+        # Running mean and running var
+        self.batch_norm_stats = get_parameters_by_name(self.critic, ["running_"])
+        # Target entropy is used when learning the entropy coefficient
+        if self.target_entropy == "auto":
+            # automatically set target entropy if needed
+            self.target_entropy = float(-np.prod(self.env.action_space.shape).astype(np.float32))  # type: ignore
+        else:
+            # Force conversion
+            # this will also throw an error for unexpected string
+            self.target_entropy = float(self.target_entropy)
+
+        # The entropy coefficient or entropy can be learned automatically
+        # see Automating Entropy Adjustment for Maximum Entropy RL section
+        # of https://arxiv.org/abs/1812.05905
+        if isinstance(self.ent_coef, str) and self.ent_coef.startswith("auto"):
+            # Default initial value of ent_coef when learned
+            init_value = 1.0
+            if "_" in self.ent_coef:
+                init_value = float(self.ent_coef.split("_")[1])
+                assert init_value > 0.0, "The initial value of ent_coef must be greater than 0"
+
+            # Note: we optimize the log of the entropy coeff which is slightly different from the paper
+            # as discussed in https://github.com/rail-berkeley/softlearning/issues/37
+            self.log_ent_coef = th.log(th.ones(1, device=self.device) * init_value).requires_grad_(True)
+            self.ent_coef_optimizer = th.optim.Adam([self.log_ent_coef], lr=self.lr_schedule(1))
+        else:
+            # Force conversion to float
+            # this will throw an error if a malformed string (different from 'auto')
+            # is passed
+            self.ent_coef_tensor = th.tensor(float(self.ent_coef), device=self.device)
+
+    def _create_aliases(self) -> None:
+        self.actor = self.policy.actor
+        self.critic = self.policy.critic
+
+    def train(self, gradient_steps: int, batch_size: int = 64) -> None:
+        # Switch to train mode (this affects batch norm / dropout)
+        self.policy.set_training_mode(True)
+        # Update optimizers learning rate
+        optimizers = [self.actor.optimizer, self.critic.optimizer]
+        if self.ent_coef_optimizer is not None:
+            optimizers += [self.ent_coef_optimizer]
+
+        # Update learning rate according to lr schedule
+        self._update_learning_rate(optimizers)
+
+        ent_coef_losses, ent_coefs = [], []
+        actor_losses, critic_losses = [], []
+
+        for gradient_step in range(gradient_steps):
+            # Sample replay buffer
+            replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)  # type: ignore[union-attr]
+
+            # We need to sample because `log_std` may have changed between two gradient steps
+            if self.use_sde:
+                self.actor.reset_noise()
+
+            # Note, in the following lines we always need to make sure to set train/eval modes
+            # of actor and critic carefully. This is because of the BatchNorm layers in the networks
+            # which behave differently in train and eval modes.
+            self.actor.set_training_mode(True)
+            actions_pi, log_prob = self.actor.action_log_prob(replay_data.observations)
+            log_prob = log_prob.reshape(-1, 1)
+            self.actor.set_training_mode(False)
+
+            ent_coef_loss = None
+            if self.ent_coef_optimizer is not None and self.log_ent_coef is not None:
+                # Important: detach the variable from the graph
+                # so we don't change it with other losses
+                # see https://github.com/rail-berkeley/softlearning/issues/60
+                ent_coef = th.exp(self.log_ent_coef.detach())
+                ent_coef_loss = -(self.log_ent_coef * (log_prob + self.target_entropy).detach()).mean()
+                ent_coef_losses.append(ent_coef_loss.item())
+            else:
+                ent_coef = self.ent_coef_tensor
+
+            ent_coefs.append(ent_coef.item())
+
+            # Optimize entropy coefficient, also called
+            # entropy temperature or alpha in the paper
+            if ent_coef_loss is not None and self.ent_coef_optimizer is not None:
+                self.ent_coef_optimizer.zero_grad()
+                ent_coef_loss.backward()
+                self.ent_coef_optimizer.step()
+
+            with th.no_grad():
+                # Select action according to policy
+                self.actor.set_training_mode(False)
+                next_actions, next_log_prob = self.actor.action_log_prob(replay_data.next_observations)
+                self.actor.set_training_mode(False)
+
+            # Joint forward pass of obs/next_obs and actions/next_state_actions to have only
+            # one forward pass with shape (n_critics, 2 * batch_size, 1).
+            #
+            # This has two reasons:
+            # 1. According to the paper obs/actions and next_obs/next_state_actions are differently
+            #    distributed which is the reason why "naively" applying Batch Normalization in SAC fails.
+            #    The batch statistics have to instead be calculated for the mixture distribution of obs/next_obs
+            #    and actions/next_state_actions. Otherwise, next_obs/next_state_actions are perceived as
+            #    out-of-distribution to the Batch Normalization layer, since running statistics are only polyak averaged
+            #    over from the live network and have never seen the next batch which is known to be unstable.
+            #    Without target networks, the joint forward pass is a simple solution to calculate
+            #    the joint batch statistics directly with a single forward pass.
+            #
+            # 2. From a computational perspective a single forward pass is simply more efficient than
+            #    two sequential forward passes.
+            all_obs = th.cat([replay_data.observations, replay_data.next_observations], dim=0)
+            all_acts = th.cat([replay_data.actions, next_actions], dim=0)
+
+            self.critic.set_training_mode(True)
+            all_q_values = th.cat(self.critic(all_obs, all_acts), dim=1)
+            self.critic.set_training_mode(False)
+            current_q_values, next_q_values = th.split(all_q_values, batch_size, dim=0)
+            current_q_values = th.split(current_q_values, [1, 1], dim=1)  # type: ignore
+
+            with th.no_grad():
+                # Compute the target Q value
+                next_q_values = next_q_values.detach()
+                next_q_values, _ = th.min(next_q_values, dim=1, keepdim=True)
+
+                # add entropy term
+                next_q_values = next_q_values - ent_coef * next_log_prob.reshape(-1, 1)
+                # td error + entropy term
+                target_q_values = replay_data.rewards + (1 - replay_data.dones) * self.gamma * next_q_values
+
+            # Compute critic loss
+            critic_loss = 0.5 * sum(F.mse_loss(current_q, target_q_values.detach()) for current_q in current_q_values)
+            assert isinstance(critic_loss, th.Tensor)  # for type checker
+            critic_losses.append(critic_loss.item())  # type: ignore[union-attr]
+
+            # Optimize the critic
+            self.critic.optimizer.zero_grad()
+            critic_loss.backward()
+            self.critic.optimizer.step()
+
+            # Compute actor loss
+            self.critic.set_training_mode(False)
+            q_values_pi = th.cat(self.critic(replay_data.observations, actions_pi), dim=1)
+            self.critic.set_training_mode(False)
+
+            min_qf_pi, _ = th.min(q_values_pi, dim=1, keepdim=True)
+            actor_loss = (ent_coef * log_prob.reshape(-1, 1) - min_qf_pi).mean()
+            actor_losses.append(actor_loss.item())
+
+            # Optimize the actor
+            self.actor.optimizer.zero_grad()
+            actor_loss.backward()
+            self.actor.optimizer.step()
+
+        self._n_updates += gradient_steps
+
+        self.logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
+        self.logger.record("train/ent_coef", np.mean(ent_coefs))
+        self.logger.record("train/actor_loss", np.mean(actor_losses))
+        self.logger.record("train/critic_loss", np.mean(critic_losses))
+        if len(ent_coef_losses) > 0:
+            self.logger.record("train/ent_coef_loss", np.mean(ent_coef_losses))
+
+    def learn(
+        self: SelfCrossQ,
+        total_timesteps: int,
+        callback: MaybeCallback = None,
+        log_interval: int = 4,
+        tb_log_name: str = "CrossQ",
+        reset_num_timesteps: bool = True,
+        progress_bar: bool = False,
+    ) -> SelfCrossQ:
+        return super().learn(
+            total_timesteps=total_timesteps,
+            callback=callback,
+            log_interval=log_interval,
+            tb_log_name=tb_log_name,
+            reset_num_timesteps=reset_num_timesteps,
+            progress_bar=progress_bar,
+        )
+
+    def _excluded_save_params(self) -> List[str]:
+        return super()._excluded_save_params() + ["actor", "critic"]
+
+    def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
+        state_dicts = ["policy", "actor.optimizer", "critic.optimizer"]
+        if self.ent_coef_optimizer is not None:
+            saved_pytorch_variables = ["log_ent_coef"]
+            state_dicts.append("ent_coef_optimizer")
+        else:
+            saved_pytorch_variables = ["ent_coef_tensor"]
+        return state_dicts, saved_pytorch_variables
diff --git a/sb3_contrib/crossq/policies.py b/sb3_contrib/crossq/policies.py
new file mode 100644
index 00000000..12b3f92b
--- /dev/null
+++ b/sb3_contrib/crossq/policies.py
@@ -0,0 +1,450 @@
+from typing import Any, Dict, List, Optional, Tuple, Type, Union
+
+import torch as th
+from gymnasium import spaces
+from stable_baselines3.common.distributions import SquashedDiagGaussianDistribution, StateDependentNoiseDistribution
+from stable_baselines3.common.policies import BaseModel, BasePolicy
+from stable_baselines3.common.preprocessing import get_action_dim
+from stable_baselines3.common.torch_layers import (
+    BaseFeaturesExtractor,
+    CombinedExtractor,
+    FlattenExtractor,
+    NatureCNN,
+    create_mlp,
+    get_actor_critic_arch,
+)
+from stable_baselines3.common.type_aliases import PyTorchObs, Schedule
+from torch import nn
+
+from sb3_contrib.common.network_layers import BatchRenorm1d
+
+# CAP the standard deviation of the actor
+LOG_STD_MAX = 2
+LOG_STD_MIN = -20
+
+
+class Actor(BasePolicy):
+    """
+    Actor network (policy) for CrossQ.
+    It contains BatchRenorm layers to stabilize and accelerate training.
+
+    :param observation_space: Obervation space
+    :param action_space: Action space
+    :param net_arch: Network architecture
+    :param features_extractor: Network to extract features
+        (a CNN when using images, a nn.Flatten() layer otherwise)
+    :param features_dim: Number of features
+    :param activation_fn: Activation function
+    :param use_sde: Whether to use State Dependent Exploration or not
+    :param log_std_init: Initial value for the log standard deviation
+    :param full_std: Whether to use (n_features x n_actions) parameters
+        for the std instead of only (n_features,) when using gSDE.
+    :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure
+        a positive standard deviation (cf paper). It allows to keep variance
+        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
+    :param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.
+    :param normalize_images: Whether to normalize images or not,
+         dividing by 255.0 (True by default)
+    """
+
+    action_space: spaces.Box
+
+    def __init__(
+        self,
+        observation_space: spaces.Space,
+        action_space: spaces.Box,
+        net_arch: List[int],
+        features_extractor: nn.Module,
+        features_dim: int,
+        activation_fn: Type[nn.Module] = nn.ReLU,
+        use_sde: bool = False,
+        log_std_init: float = -3,
+        full_std: bool = True,
+        use_expln: bool = False,
+        clip_mean: float = 2.0,
+        normalize_images: bool = True,
+        batch_norm: bool = True,
+    ):
+        super().__init__(
+            observation_space,
+            action_space,
+            features_extractor=features_extractor,
+            normalize_images=normalize_images,
+            squash_output=True,
+        )
+
+        # Save arguments to re-create object at loading
+        self.use_sde = use_sde
+        self.sde_features_extractor = None
+        self.net_arch = net_arch
+        self.features_dim = features_dim
+        self.activation_fn = activation_fn
+        self.log_std_init = log_std_init
+        self.use_expln = use_expln
+        self.full_std = full_std
+        self.clip_mean = clip_mean
+
+        action_dim = get_action_dim(self.action_space)
+        latent_pi_net = create_mlp(features_dim, -1, net_arch, activation_fn)
+
+        if batch_norm:
+            # If batchnorm, then we want to add torch.nn.Batch_Norm layers before every linear layer
+            net : List[Union[nn.Module, BatchRenorm1d]] = []
+            for layer in latent_pi_net:
+                if isinstance(layer, nn.Linear):
+                    net.append(BatchRenorm1d(layer.in_features, eps=0.001, momentum=0.01))
+                net.append(layer)
+            net.append(BatchRenorm1d(net_arch[-1], eps=0.001, momentum=0.01))
+            latent_pi_net = net
+
+        self.latent_pi = nn.Sequential(*latent_pi_net)
+        last_layer_dim = net_arch[-1] if len(net_arch) > 0 else features_dim
+
+        if self.use_sde:
+            self.action_dist = StateDependentNoiseDistribution(
+                action_dim, full_std=full_std, use_expln=use_expln, learn_features=True, squash_output=True
+            )
+            self.mu, self.log_std = self.action_dist.proba_distribution_net(
+                latent_dim=last_layer_dim, latent_sde_dim=last_layer_dim, log_std_init=log_std_init
+            )
+            # Avoid numerical issues by limiting the mean of the Gaussian
+            # to be in [-clip_mean, clip_mean]
+            if clip_mean > 0.0:
+                self.mu = nn.Sequential(self.mu, nn.Hardtanh(min_val=-clip_mean, max_val=clip_mean))
+        else:
+            self.action_dist = SquashedDiagGaussianDistribution(action_dim)  # type: ignore[assignment]
+            self.mu = nn.Linear(last_layer_dim, action_dim)
+            self.log_std = nn.Linear(last_layer_dim, action_dim)  # type: ignore[assignment]
+
+    def _get_constructor_parameters(self) -> Dict[str, Any]:
+        data = super()._get_constructor_parameters()
+
+        data.update(
+            dict(
+                net_arch=self.net_arch,
+                features_dim=self.features_dim,
+                activation_fn=self.activation_fn,
+                use_sde=self.use_sde,
+                log_std_init=self.log_std_init,
+                full_std=self.full_std,
+                use_expln=self.use_expln,
+                features_extractor=self.features_extractor,
+                clip_mean=self.clip_mean,
+            )
+        )
+        return data
+
+    def get_std(self) -> th.Tensor:
+        """
+        Retrieve the standard deviation of the action distribution.
+        Only useful when using gSDE.
+        It corresponds to ``th.exp(log_std)`` in the normal case,
+        but is slightly different when using ``expln`` function
+        (cf StateDependentNoiseDistribution doc).
+
+        :return:
+        """
+        msg = "get_std() is only available when using gSDE"
+        assert isinstance(self.action_dist, StateDependentNoiseDistribution), msg
+        return self.action_dist.get_std(self.log_std)
+
+    def reset_noise(self, batch_size: int = 1) -> None:
+        """
+        Sample new weights for the exploration matrix, when using gSDE.
+
+        :param batch_size:
+        """
+        msg = "reset_noise() is only available when using gSDE"
+        assert isinstance(self.action_dist, StateDependentNoiseDistribution), msg
+        self.action_dist.sample_weights(self.log_std, batch_size=batch_size)
+
+    def get_action_dist_params(self, obs: PyTorchObs) -> Tuple[th.Tensor, th.Tensor, Dict[str, th.Tensor]]:
+        """
+        Get the parameters for the action distribution.
+
+        :param obs:
+        :return:
+            Mean, standard deviation and optional keyword arguments.
+        """
+        features = self.extract_features(obs, self.features_extractor)
+        latent_pi = self.latent_pi(features)
+        mean_actions = self.mu(latent_pi)
+
+        if self.use_sde:
+            return mean_actions, self.log_std, dict(latent_sde=latent_pi)
+        # Unstructured exploration (Original implementation)
+        log_std = self.log_std(latent_pi)  # type: ignore[operator]
+        # Original Implementation to cap the standard deviation
+        log_std = th.clamp(log_std, LOG_STD_MIN, LOG_STD_MAX)
+        return mean_actions, log_std, {}
+
+    def forward(self, obs: PyTorchObs, deterministic: bool = False) -> th.Tensor:
+        mean_actions, log_std, kwargs = self.get_action_dist_params(obs)
+        # Note: the action is squashed
+        return self.action_dist.actions_from_params(mean_actions, log_std, deterministic=deterministic, **kwargs)
+
+    def action_log_prob(self, obs: PyTorchObs) -> Tuple[th.Tensor, th.Tensor]:
+        mean_actions, log_std, kwargs = self.get_action_dist_params(obs)
+        # return action and associated log prob
+        return self.action_dist.log_prob_from_params(mean_actions, log_std, **kwargs)
+
+    def _predict(self, observation: PyTorchObs, deterministic: bool = False) -> th.Tensor:
+        return self(observation, deterministic)
+
+
+class CrossQCritic(BaseModel):
+    """
+    Critic network(s) for CrossQ.
+    The differnce with standard critic networks used by SAC/TD3 is that it uses BatchRenorm layers.
+
+    By default, it creates two critic networks used to reduce overestimation
+    thanks to clipped Q-learning (cf TD3 paper).
+
+    :param observation_space: Obervation space
+    :param action_space: Action space
+    :param net_arch: Network architecture
+    :param features_extractor: Network to extract features
+        (a CNN when using images, a nn.Flatten() layer otherwise)
+    :param features_dim: Number of features
+    :param activation_fn: Activation function
+    :param normalize_images: Whether to normalize images or not,
+         dividing by 255.0 (True by default)
+    :param n_critics: Number of critic networks to create.
+    :param share_features_extractor: Whether the features extractor is shared or not
+        between the actor and the critic (this saves computation time)
+    """
+
+    features_extractor: BaseFeaturesExtractor
+
+    def __init__(
+        self,
+        observation_space: spaces.Space,
+        action_space: spaces.Box,
+        net_arch: List[int],
+        features_extractor: BaseFeaturesExtractor,
+        features_dim: int,
+        activation_fn: Type[nn.Module] = nn.ReLU,
+        normalize_images: bool = True,
+        n_critics: int = 2,
+        share_features_extractor: bool = True,
+        batch_norm: bool = True,
+    ):
+        super().__init__(
+            observation_space,
+            action_space,
+            features_extractor=features_extractor,
+            normalize_images=normalize_images,
+        )
+
+        action_dim = get_action_dim(self.action_space)
+
+        self.share_features_extractor = share_features_extractor
+        self.n_critics = n_critics
+        self.q_networks: List[nn.Module] = []
+        for idx in range(n_critics):
+            q_net_list = create_mlp(features_dim + action_dim, 1, net_arch, activation_fn)
+
+            if batch_norm:
+                # If batchnorm, then we want to add torch.nn.Batch_Norm layers before every linear layer
+                net : List[Union[nn.Module, BatchRenorm1d]] = []
+                for layer in q_net_list:
+                    if isinstance(layer, nn.Linear):
+                        net.append(BatchRenorm1d(layer.in_features, eps=0.001, momentum=0.01))
+                    net.append(layer)
+                q_net_list = net
+
+            q_net = nn.Sequential(*q_net_list)
+            self.add_module(f"qf{idx}", q_net)
+            self.q_networks.append(q_net)
+
+    def forward(self, obs: th.Tensor, actions: th.Tensor) -> Tuple[th.Tensor, ...]:
+        # Learn the features extractor using the policy loss only
+        # when the features_extractor is shared with the actor
+        with th.set_grad_enabled(not self.share_features_extractor):
+            features = self.extract_features(obs, self.features_extractor)
+        qvalue_input = th.cat([features, actions], dim=1)
+        return tuple(q_net(qvalue_input) for q_net in self.q_networks)
+
+
+class CrossQPolicy(BasePolicy):
+    """
+    Policy class (with both actor and critic) for CrossQ.
+
+    :param observation_space: Observation space
+    :param action_space: Action space
+    :param lr_schedule: Learning rate schedule (could be constant)
+    :param net_arch: The specification of the policy and value networks.
+    :param activation_fn: Activation function
+    :param use_sde: Whether to use State Dependent Exploration or not
+    :param log_std_init: Initial value for the log standard deviation
+    :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure
+        a positive standard deviation (cf paper). It allows to keep variance
+        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
+    :param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.
+    :param features_extractor_class: Features extractor to use.
+    :param features_extractor_kwargs: Keyword arguments
+        to pass to the features extractor.
+    :param normalize_images: Whether to normalize images or not,
+         dividing by 255.0 (True by default)
+    :param optimizer_class: The optimizer to use,
+        ``th.optim.Adam`` by default
+    :param optimizer_kwargs: Additional keyword arguments,
+        excluding the learning rate, to pass to the optimizer
+    :param n_critics: Number of critic networks to create.
+    :param share_features_extractor: Whether to share or not the features extractor
+        between the actor and the critic (this saves computation time)
+    """
+
+    actor: Actor
+    critic: CrossQCritic
+
+    def __init__(
+        self,
+        observation_space: spaces.Space,
+        action_space: spaces.Box,
+        lr_schedule: Schedule,
+        net_arch: Optional[Union[List[int], Dict[str, List[int]]]] = None,
+        activation_fn: Type[nn.Module] = nn.ReLU,
+        use_sde: bool = False,
+        log_std_init: float = -3,
+        use_expln: bool = False,
+        clip_mean: float = 2.0,
+        features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
+        features_extractor_kwargs: Optional[Dict[str, Any]] = None,
+        normalize_images: bool = True,
+        optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
+        optimizer_kwargs: Optional[Dict[str, Any]] = None,
+        n_critics: int = 2,
+        share_features_extractor: bool = False,
+    ):
+        super().__init__(
+            observation_space,
+            action_space,
+            features_extractor_class,
+            features_extractor_kwargs,
+            optimizer_class=optimizer_class,
+            optimizer_kwargs=optimizer_kwargs,
+            squash_output=True,
+            normalize_images=normalize_images,
+        )
+
+        if net_arch is None:
+            net_arch = {"pi": [256, 256], "qf": [2048, 2048]}
+
+        actor_arch, critic_arch = get_actor_critic_arch(net_arch)
+
+        self.net_arch = net_arch
+        self.activation_fn = activation_fn
+        self.net_args = {
+            "observation_space": self.observation_space,
+            "action_space": self.action_space,
+            "net_arch": actor_arch,
+            "activation_fn": self.activation_fn,
+            "normalize_images": normalize_images,
+            "batch_norm": True,
+        }
+        self.actor_kwargs = self.net_args.copy()
+
+        sde_kwargs = {
+            "use_sde": use_sde,
+            "log_std_init": log_std_init,
+            "use_expln": use_expln,
+            "clip_mean": clip_mean,
+        }
+        self.actor_kwargs.update(sde_kwargs)
+        self.critic_kwargs = self.net_args.copy()
+        self.critic_kwargs.update(
+            {
+                "n_critics": n_critics,
+                "net_arch": critic_arch,
+                "share_features_extractor": share_features_extractor,
+                "batch_norm": True,
+            }
+        )
+
+        self.share_features_extractor = share_features_extractor
+
+        self._build(lr_schedule)
+
+    def _build(self, lr_schedule: Schedule) -> None:
+        self.actor = self.make_actor()
+        self.actor.optimizer = self.optimizer_class(
+            self.actor.parameters(),
+            lr=lr_schedule(1),  # type: ignore[call-arg]
+            **self.optimizer_kwargs,
+        )
+
+        if self.share_features_extractor:
+            self.critic = self.make_critic(features_extractor=self.actor.features_extractor)
+            # Do not optimize the shared features extractor with the critic loss
+            # otherwise, there are gradient computation issues
+            critic_parameters = [param for name, param in self.critic.named_parameters() if "features_extractor" not in name]
+        else:
+            # Create a separate features extractor for the critic
+            # this requires more memory and computation
+            self.critic = self.make_critic(features_extractor=None)
+            critic_parameters = list(self.critic.parameters())
+
+        self.critic.optimizer = self.optimizer_class(
+            critic_parameters,
+            lr=lr_schedule(1),  # type: ignore[call-arg]
+            **self.optimizer_kwargs,
+        )
+
+    def _get_constructor_parameters(self) -> Dict[str, Any]:
+        data = super()._get_constructor_parameters()
+
+        data.update(
+            dict(
+                net_arch=self.net_arch,
+                activation_fn=self.net_args["activation_fn"],
+                use_sde=self.actor_kwargs["use_sde"],
+                log_std_init=self.actor_kwargs["log_std_init"],
+                use_expln=self.actor_kwargs["use_expln"],
+                clip_mean=self.actor_kwargs["clip_mean"],
+                n_critics=self.critic_kwargs["n_critics"],
+                lr_schedule=self._dummy_schedule,  # dummy lr schedule, not needed for loading policy alone
+                optimizer_class=self.optimizer_class,
+                optimizer_kwargs=self.optimizer_kwargs,
+                features_extractor_class=self.features_extractor_class,
+                features_extractor_kwargs=self.features_extractor_kwargs,
+            )
+        )
+        return data
+
+    def reset_noise(self, batch_size: int = 1) -> None:
+        """
+        Sample new weights for the exploration matrix, when using gSDE.
+
+        :param batch_size:
+        """
+        self.actor.reset_noise(batch_size=batch_size)
+
+    def make_actor(self, features_extractor: Optional[BaseFeaturesExtractor] = None) -> Actor:
+        actor_kwargs = self._update_features_extractor(self.actor_kwargs, features_extractor)
+        return Actor(**actor_kwargs).to(self.device)
+
+    def make_critic(self, features_extractor: Optional[BaseFeaturesExtractor] = None) -> CrossQCritic:
+        critic_kwargs = self._update_features_extractor(self.critic_kwargs, features_extractor)
+        return CrossQCritic(**critic_kwargs).to(self.device)
+
+    def forward(self, obs: PyTorchObs, deterministic: bool = False) -> th.Tensor:
+        return self._predict(obs, deterministic=deterministic)
+
+    def _predict(self, observation: PyTorchObs, deterministic: bool = False) -> th.Tensor:
+        return self.actor(observation, deterministic)
+
+    def set_training_mode(self, mode: bool) -> None:
+        """
+        Put the policy in either training or evaluation mode.
+
+        This affects certain modules, such as batch normalisation and dropout.
+
+        :param mode: if true, set to training mode, else set to evaluation mode
+        """
+        self.actor.set_training_mode(mode)
+        self.critic.set_training_mode(mode)
+        self.training = mode
+
+
+MlpPolicy = CrossQPolicy
diff --git a/tests/test_run.py b/tests/test_run.py
index a8cec79a..ed524f22 100644
--- a/tests/test_run.py
+++ b/tests/test_run.py
@@ -3,11 +3,21 @@
 from stable_baselines3.common.env_util import make_vec_env
 from stable_baselines3.common.vec_env import VecNormalize
 
-from sb3_contrib import ARS, QRDQN, TQC, TRPO, MaskablePPO
+from sb3_contrib import ARS, QRDQN, TQC, TRPO, CrossQ, MaskablePPO
 from sb3_contrib.common.envs import InvalidActionEnvDiscrete
 from sb3_contrib.common.vec_env import AsyncEval
 
 
+def test():
+    model = CrossQ(
+        "MlpPolicy",
+        "Pendulum-v1",
+        learning_starts=100,
+        verbose=1,
+    )
+    model.learn(total_timesteps=300)
+
+
 @pytest.mark.parametrize("ent_coef", ["auto", 0.01, "auto_0.01"])
 def test_tqc(ent_coef):
     model = TQC(

From 4fa78a79fefa4cb81564b8e591bca514739318e6 Mon Sep 17 00:00:00 2001
From: Daniel Palenicek <daniel.palenicek@tu-darmstadt.de>
Date: Sun, 5 May 2024 11:57:53 +0200
Subject: [PATCH 02/32] Fixed code style

---
 sb3_contrib/common/network_layers.py | 4 ----
 sb3_contrib/crossq/crossq.py         | 6 +++---
 sb3_contrib/crossq/policies.py       | 6 ++----
 3 files changed, 5 insertions(+), 11 deletions(-)

diff --git a/sb3_contrib/common/network_layers.py b/sb3_contrib/common/network_layers.py
index 524c5274..28aab64d 100644
--- a/sb3_contrib/common/network_layers.py
+++ b/sb3_contrib/common/network_layers.py
@@ -3,10 +3,6 @@
 __all__ = ["BatchRenorm1d"]
 
 
-import torch
-import torch.nn as nn
-
-
 class BatchRenorm(torch.jit.ScriptModule):
     """
     BatchRenorm Module (https://arxiv.org/abs/1702.03275).
diff --git a/sb3_contrib/crossq/crossq.py b/sb3_contrib/crossq/crossq.py
index de739af5..54d31b61 100644
--- a/sb3_contrib/crossq/crossq.py
+++ b/sb3_contrib/crossq/crossq.py
@@ -6,12 +6,12 @@
 from stable_baselines3.common.buffers import ReplayBuffer
 from stable_baselines3.common.noise import ActionNoise
 from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
-from stable_baselines3.common.policies import BasePolicy, ContinuousCritic
+from stable_baselines3.common.policies import BasePolicy
 from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
-from stable_baselines3.common.utils import get_parameters_by_name, polyak_update
+from stable_baselines3.common.utils import get_parameters_by_name
 from torch.nn import functional as F
 
-from sb3_contrib.crossq.policies import Actor, CrossQPolicy, CrossQCritic, MlpPolicy
+from sb3_contrib.crossq.policies import Actor, CrossQCritic, CrossQPolicy, MlpPolicy
 
 SelfCrossQ = TypeVar("SelfCrossQ", bound="CrossQ")
 
diff --git a/sb3_contrib/crossq/policies.py b/sb3_contrib/crossq/policies.py
index 12b3f92b..deb588a4 100644
--- a/sb3_contrib/crossq/policies.py
+++ b/sb3_contrib/crossq/policies.py
@@ -7,9 +7,7 @@
 from stable_baselines3.common.preprocessing import get_action_dim
 from stable_baselines3.common.torch_layers import (
     BaseFeaturesExtractor,
-    CombinedExtractor,
     FlattenExtractor,
-    NatureCNN,
     create_mlp,
     get_actor_critic_arch,
 )
@@ -89,7 +87,7 @@ def __init__(
 
         if batch_norm:
             # If batchnorm, then we want to add torch.nn.Batch_Norm layers before every linear layer
-            net : List[Union[nn.Module, BatchRenorm1d]] = []
+            net: List[Union[nn.Module, BatchRenorm1d]] = []
             for layer in latent_pi_net:
                 if isinstance(layer, nn.Linear):
                     net.append(BatchRenorm1d(layer.in_features, eps=0.001, momentum=0.01))
@@ -246,7 +244,7 @@ def __init__(
 
             if batch_norm:
                 # If batchnorm, then we want to add torch.nn.Batch_Norm layers before every linear layer
-                net : List[Union[nn.Module, BatchRenorm1d]] = []
+                net: List[Union[nn.Module, BatchRenorm1d]] = []
                 for layer in q_net_list:
                     if isinstance(layer, nn.Linear):
                         net.append(BatchRenorm1d(layer.in_features, eps=0.001, momentum=0.01))

From 7ce57de8b5afffde52df0a61a2abca7880903c34 Mon Sep 17 00:00:00 2001
From: Daniel Palenicek <daniel.palenicek@tu-darmstadt.de>
Date: Sun, 12 May 2024 02:48:24 +0200
Subject: [PATCH 03/32] Clean up, comments and refactored to sbx variable names

---
 sb3_contrib/common/network_layers.py | 61 +++++++++++++++++++---------
 1 file changed, 42 insertions(+), 19 deletions(-)

diff --git a/sb3_contrib/common/network_layers.py b/sb3_contrib/common/network_layers.py
index 28aab64d..5bb3b263 100644
--- a/sb3_contrib/common/network_layers.py
+++ b/sb3_contrib/common/network_layers.py
@@ -6,21 +6,26 @@
 class BatchRenorm(torch.jit.ScriptModule):
     """
     BatchRenorm Module (https://arxiv.org/abs/1702.03275).
-    Adapted from flax.linen.normalization.BatchNorm
+    Adapted to Pytorch from sbx.sbx.common.jax_layers.BatchRenorm
 
     BatchRenorm is an improved version of vanilla BatchNorm. Contrary to BatchNorm,
     BatchRenorm uses the running statistics for normalizing the batches after a warmup phase.
     This makes it less prone to suffer from "outlier" batches that can happen
     during very long training runs and, therefore, is more robust during long training runs.
 
-    During the warmup phase, it behaves exactly like a BatchNorm layer.
+    During the warmup phase, it behaves exactly like a BatchNorm layer. After the warmup phase,
+    the running statistics are used for normalization. The running statistics are updated during
+    training mode. During evaluation mode, the running statistics are used for normalization but
+    not updated.
 
     Args:
         num_features: Number of features in the input tensor.
         eps: A value added to the variance for numerical stability.
-        momentum: The value used for the running_mean and running_var computation.
+        momentum: The value used for the ra_mean and ra_var computation.
         affine: A boolean value that when set to True, this module has learnable
             affine parameters. Default: True
+        warmup_steps: Number of warum steps that are performed before the running statistics
+            are used form normalization. During the warump phase, the batch statistics are used.
     """
 
     def __init__(
@@ -29,11 +34,12 @@ def __init__(
         eps: float = 0.001,
         momentum: float = 0.01,
         affine: bool = True,
+        warmup_steps: int = 100_000,
     ):
         super().__init__()
-        self.register_buffer("running_mean", torch.zeros(num_features, dtype=torch.float))
-        self.register_buffer("running_var", torch.ones(num_features, dtype=torch.float))
-        self.register_buffer("num_batches_tracked", torch.tensor(0, dtype=torch.long))
+        self.register_buffer("ra_mean", torch.zeros(num_features, dtype=torch.float))
+        self.register_buffer("ra_var", torch.ones(num_features, dtype=torch.float))
+        self.register_buffer("steps", torch.tensor(0, dtype=torch.long))
         self.scale = torch.nn.Parameter(torch.ones(num_features, dtype=torch.float))
         self.bias = torch.nn.Parameter(torch.zeros(num_features, dtype=torch.float))
 
@@ -43,11 +49,21 @@ def __init__(
         self.momentum = momentum
         self.rmax = 3.0
         self.dmax = 5.0
+        self.warmup_steps = warmup_steps
 
     def _check_input_dim(self, x: torch.Tensor) -> None:
         raise NotImplementedError()
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Normalize the input tensor.
+        
+        Args:
+            x: Input tensor
+        
+        Returns:
+            Normalized tensor.
+        """
 
         if self.training:
             batch_mean = x.mean(0)
@@ -55,32 +71,39 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
             batch_std = (batch_var + self.eps).sqrt()
 
             # Use batch statistics during initial warm up phase.
-            if self.num_batches_tracked > 100_000:
-
-                running_std = (self.running_var + self.eps).sqrt()
-                running_mean = self.running_mean
+            # Note: in the original paper, after some warmup phase (batch norm phase of 5k steps)
+            # the constraints are linearly relaxed to r_max/d_max over 40k steps
+            # Here we only have a warmup phase
+            if self.steps > self.warmup_steps:
 
+                running_std = (self.ra_var + self.eps).sqrt()
+                # scale
                 r = (batch_std / running_std).detach()
                 r = r.clamp(1 / self.rmax, self.rmax)
-                d = ((batch_mean - running_mean) / running_std).detach()
+                # bias
+                d = ((batch_mean - self.ra_mean) / running_std).detach()
                 d = d.clamp(-self.dmax, self.dmax)
 
-                m = batch_mean - d * batch_var.sqrt() / r
-                v = batch_var / (r**2)
+                # BatchNorm normalization, using minibatch stats and running average stats
+                # Because we use _normalize, this is equivalent to
+                # ((x - x_mean) / sigma) * r + d = ((x - x_mean) * r + d * sigma) / sigma
+                # where sigma = sqrt(var)
+                custom_mean = batch_mean - d * batch_var.sqrt() / r
+                custom_var = batch_var / (r**2)
 
             else:
-                m, v = batch_mean, batch_var
+                custom_mean, custom_var = batch_mean, batch_var
 
             # Update Running Statistics
-            self.running_mean += self.momentum * (batch_mean.detach() - self.running_mean)
-            self.running_var += self.momentum * (batch_var.detach() - self.running_var)
-            self.num_batches_tracked += 1
+            self.ra_mean += self.momentum * (batch_mean.detach() - self.ra_mean)
+            self.ra_var += self.momentum * (batch_var.detach() - self.ra_var)
+            self.steps += 1
 
         else:
-            m, v = self.running_mean, self.running_var
+            custom_mean, custom_var = self.ra_mean, self.ra_var
 
         # Normalize
-        x = (x - m[None]) / (v[None] + self.eps).sqrt()
+        x = (x - custom_mean[None]) / (custom_var[None] + self.eps).sqrt()
 
         if self.affine:
             x = self.scale * x + self.bias

From 9c339b881a631875b1c139f2a0eb0eb08945cdb7 Mon Sep 17 00:00:00 2001
From: Daniel Palenicek <daniel.palenicek@tu-darmstadt.de>
Date: Sun, 12 May 2024 03:13:52 +0200
Subject: [PATCH 04/32] 1024 neuron Q function (sbx default)

---
 sb3_contrib/crossq/policies.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/sb3_contrib/crossq/policies.py b/sb3_contrib/crossq/policies.py
index deb588a4..9b1c7726 100644
--- a/sb3_contrib/crossq/policies.py
+++ b/sb3_contrib/crossq/policies.py
@@ -327,7 +327,10 @@ def __init__(
         )
 
         if net_arch is None:
-            net_arch = {"pi": [256, 256], "qf": [2048, 2048]}
+            # While CrossQ already works with a [256,256] critic network,
+            # the authors found that a wider network significantly improves performance.
+            # We use a slightly smaller net for faster computation, [1024, 1024] instead of [2048, 2048] in the paper.
+            net_arch = {"pi": [256, 256], "qf": [1024, 1024]}
 
         actor_arch, critic_arch = get_actor_critic_arch(net_arch)
 

From 2b1ff5e0073256738d315b601a0e4aa06ea5c6c1 Mon Sep 17 00:00:00 2001
From: Daniel Palenicek <daniel.palenicek@tu-darmstadt.de>
Date: Sun, 12 May 2024 03:14:26 +0200
Subject: [PATCH 05/32] batch norm parameters as function arguments

---
 sb3_contrib/crossq/policies.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/sb3_contrib/crossq/policies.py b/sb3_contrib/crossq/policies.py
index 9b1c7726..7bba8190 100644
--- a/sb3_contrib/crossq/policies.py
+++ b/sb3_contrib/crossq/policies.py
@@ -62,6 +62,8 @@ def __init__(
         clip_mean: float = 2.0,
         normalize_images: bool = True,
         batch_norm: bool = True,
+        batch_norm_momentum: float = 0.01,
+        batch_norm_eps: float = 0.001
     ):
         super().__init__(
             observation_space,
@@ -90,9 +92,9 @@ def __init__(
             net: List[Union[nn.Module, BatchRenorm1d]] = []
             for layer in latent_pi_net:
                 if isinstance(layer, nn.Linear):
-                    net.append(BatchRenorm1d(layer.in_features, eps=0.001, momentum=0.01))
+                    net.append(BatchRenorm1d(layer.in_features, eps=batch_norm_eps, momentum=batch_norm_momentum))
                 net.append(layer)
-            net.append(BatchRenorm1d(net_arch[-1], eps=0.001, momentum=0.01))
+            net.append(BatchRenorm1d(net_arch[-1], eps=batch_norm_eps, momentum=batch_norm_momentum))
             latent_pi_net = net
 
         self.latent_pi = nn.Sequential(*latent_pi_net)
@@ -226,6 +228,8 @@ def __init__(
         n_critics: int = 2,
         share_features_extractor: bool = True,
         batch_norm: bool = True,
+        batch_norm_momentum: float = 0.01,
+        batch_norm_eps: float = 0.001
     ):
         super().__init__(
             observation_space,
@@ -247,7 +251,7 @@ def __init__(
                 net: List[Union[nn.Module, BatchRenorm1d]] = []
                 for layer in q_net_list:
                     if isinstance(layer, nn.Linear):
-                        net.append(BatchRenorm1d(layer.in_features, eps=0.001, momentum=0.01))
+                        net.append(BatchRenorm1d(layer.in_features, eps=batch_norm_eps, momentum=batch_norm_momentum))
                     net.append(layer)
                 q_net_list = net
 

From aace2acc195b5552b0b51b74f6f7573bef11aba3 Mon Sep 17 00:00:00 2001
From: Daniel Palenicek <daniel.palenicek@tu-darmstadt.de>
Date: Sun, 12 May 2024 03:15:54 +0200
Subject: [PATCH 06/32] clean up. reshape instead of split

---
 sb3_contrib/crossq/crossq.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sb3_contrib/crossq/crossq.py b/sb3_contrib/crossq/crossq.py
index 54d31b61..98982d11 100644
--- a/sb3_contrib/crossq/crossq.py
+++ b/sb3_contrib/crossq/crossq.py
@@ -254,7 +254,7 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
             all_q_values = th.cat(self.critic(all_obs, all_acts), dim=1)
             self.critic.set_training_mode(False)
             current_q_values, next_q_values = th.split(all_q_values, batch_size, dim=0)
-            current_q_values = th.split(current_q_values, [1, 1], dim=1)  # type: ignore
+            current_q_values = current_q_values.T[..., None]
 
             with th.no_grad():
                 # Compute the target Q value

From 4df7111feaa2e76ef1125b71f2c723cd8c9d8455 Mon Sep 17 00:00:00 2001
From: Daniel Palenicek <daniel.palenicek@tu-darmstadt.de>
Date: Sun, 12 May 2024 03:16:15 +0200
Subject: [PATCH 07/32] Added policy delay

---
 sb3_contrib/crossq/crossq.py | 32 +++++++++++++++++++-------------
 1 file changed, 19 insertions(+), 13 deletions(-)

diff --git a/sb3_contrib/crossq/crossq.py b/sb3_contrib/crossq/crossq.py
index 98982d11..2848bf0e 100644
--- a/sb3_contrib/crossq/crossq.py
+++ b/sb3_contrib/crossq/crossq.py
@@ -89,6 +89,7 @@ def __init__(
         optimize_memory_usage: bool = False,
         ent_coef: Union[str, float] = "auto",
         target_entropy: Union[str, float] = "auto",
+        policy_delay: int = 3,
         use_sde: bool = False,
         sde_sample_freq: int = -1,
         use_sde_at_warmup: bool = False,
@@ -134,6 +135,7 @@ def __init__(
         # Inverse of the reward scale
         self.ent_coef = ent_coef
         self.ent_coef_optimizer: Optional[th.optim.Adam] = None
+        self.policy_delay = policy_delay
 
         if _init_setup_model:
             self._setup_model()
@@ -198,9 +200,10 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
             if self.use_sde:
                 self.actor.reset_noise()
 
-            # Note, in the following lines we always need to make sure to set train/eval modes
+            # Note: in the following lines we always need to make sure to set train/eval modes
             # of actor and critic carefully. This is because of the BatchNorm layers in the networks
             # which behave differently in train and eval modes.
+
             self.actor.set_training_mode(True)
             actions_pi, log_prob = self.actor.action_log_prob(replay_data.observations)
             log_prob = log_prob.reshape(-1, 1)
@@ -221,7 +224,9 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
 
             # Optimize entropy coefficient, also called
             # entropy temperature or alpha in the paper
-            if ent_coef_loss is not None and self.ent_coef_optimizer is not None:
+            if ent_coef_loss is not None and self.ent_coef_optimizer is not None \
+                and self._n_updates % self.policy_delay == 0:
+
                 self.ent_coef_optimizer.zero_grad()
                 ent_coef_loss.backward()
                 self.ent_coef_optimizer.step()
@@ -276,19 +281,20 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
             critic_loss.backward()
             self.critic.optimizer.step()
 
-            # Compute actor loss
-            self.critic.set_training_mode(False)
-            q_values_pi = th.cat(self.critic(replay_data.observations, actions_pi), dim=1)
-            self.critic.set_training_mode(False)
+            if self._n_updates % self.policy_delay == 0:
+                # Compute actor loss
+                self.critic.set_training_mode(False)
+                q_values_pi = th.cat(self.critic(replay_data.observations, actions_pi), dim=1)
+                self.critic.set_training_mode(False)
 
-            min_qf_pi, _ = th.min(q_values_pi, dim=1, keepdim=True)
-            actor_loss = (ent_coef * log_prob.reshape(-1, 1) - min_qf_pi).mean()
-            actor_losses.append(actor_loss.item())
+                min_qf_pi, _ = th.min(q_values_pi, dim=1, keepdim=True)
+                actor_loss = (ent_coef * log_prob.reshape(-1, 1) - min_qf_pi).mean()
+                actor_losses.append(actor_loss.item())
 
-            # Optimize the actor
-            self.actor.optimizer.zero_grad()
-            actor_loss.backward()
-            self.actor.optimizer.step()
+                # Optimize the actor
+                self.actor.optimizer.zero_grad()
+                actor_loss.backward()
+                self.actor.optimizer.step()
 
         self._n_updates += gradient_steps
 

From 5583225931daebcb603edb259b0bba7886371f11 Mon Sep 17 00:00:00 2001
From: Daniel Palenicek <daniel.palenicek@tu-darmstadt.de>
Date: Sun, 12 May 2024 15:26:16 +0200
Subject: [PATCH 08/32] fixed commit-checks

---
 sb3_contrib/common/network_layers.py | 4 ++--
 sb3_contrib/crossq/crossq.py         | 7 +++----
 sb3_contrib/crossq/policies.py       | 4 ++--
 3 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/sb3_contrib/common/network_layers.py b/sb3_contrib/common/network_layers.py
index 5bb3b263..7d1e715b 100644
--- a/sb3_contrib/common/network_layers.py
+++ b/sb3_contrib/common/network_layers.py
@@ -57,10 +57,10 @@ def _check_input_dim(self, x: torch.Tensor) -> None:
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """
         Normalize the input tensor.
-        
+
         Args:
             x: Input tensor
-        
+
         Returns:
             Normalized tensor.
         """
diff --git a/sb3_contrib/crossq/crossq.py b/sb3_contrib/crossq/crossq.py
index 2848bf0e..b5a5fd0d 100644
--- a/sb3_contrib/crossq/crossq.py
+++ b/sb3_contrib/crossq/crossq.py
@@ -192,7 +192,7 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
         ent_coef_losses, ent_coefs = [], []
         actor_losses, critic_losses = [], []
 
-        for gradient_step in range(gradient_steps):
+        for _ in range(gradient_steps):
             # Sample replay buffer
             replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)  # type: ignore[union-attr]
 
@@ -224,8 +224,7 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
 
             # Optimize entropy coefficient, also called
             # entropy temperature or alpha in the paper
-            if ent_coef_loss is not None and self.ent_coef_optimizer is not None \
-                and self._n_updates % self.policy_delay == 0:
+            if ent_coef_loss is not None and self.ent_coef_optimizer is not None and self._n_updates % self.policy_delay == 0:
 
                 self.ent_coef_optimizer.zero_grad()
                 ent_coef_loss.backward()
@@ -324,7 +323,7 @@ def learn(
         )
 
     def _excluded_save_params(self) -> List[str]:
-        return super()._excluded_save_params() + ["actor", "critic"]
+        return [*super()._excluded_save_params(), "actor", "critic"]
 
     def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
         state_dicts = ["policy", "actor.optimizer", "critic.optimizer"]
diff --git a/sb3_contrib/crossq/policies.py b/sb3_contrib/crossq/policies.py
index 7bba8190..dfd6cff0 100644
--- a/sb3_contrib/crossq/policies.py
+++ b/sb3_contrib/crossq/policies.py
@@ -63,7 +63,7 @@ def __init__(
         normalize_images: bool = True,
         batch_norm: bool = True,
         batch_norm_momentum: float = 0.01,
-        batch_norm_eps: float = 0.001
+        batch_norm_eps: float = 0.001,
     ):
         super().__init__(
             observation_space,
@@ -229,7 +229,7 @@ def __init__(
         share_features_extractor: bool = True,
         batch_norm: bool = True,
         batch_norm_momentum: float = 0.01,
-        batch_norm_eps: float = 0.001
+        batch_norm_eps: float = 0.001,
     ):
         super().__init__(
             observation_space,

From 567c2fb8a50ee3904e71e1958e6a698a7f5bfcd5 Mon Sep 17 00:00:00 2001
From: Antonin Raffin <antonin.raffin@ensta.org>
Date: Mon, 13 May 2024 10:09:28 +0200
Subject: [PATCH 09/32] Fix f-string

---
 sb3_contrib/common/network_layers.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/sb3_contrib/common/network_layers.py b/sb3_contrib/common/network_layers.py
index 7d1e715b..03d2f192 100644
--- a/sb3_contrib/common/network_layers.py
+++ b/sb3_contrib/common/network_layers.py
@@ -37,6 +37,7 @@ def __init__(
         warmup_steps: int = 100_000,
     ):
         super().__init__()
+        # Running average mean and variance
         self.register_buffer("ra_mean", torch.zeros(num_features, dtype=torch.float))
         self.register_buffer("ra_var", torch.ones(num_features, dtype=torch.float))
         self.register_buffer("steps", torch.tensor(0, dtype=torch.long))
@@ -114,4 +115,4 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 class BatchRenorm1d(BatchRenorm):
     def _check_input_dim(self, x: torch.Tensor) -> None:
         if x.dim() == 1:
-            raise ValueError("expected 2D or 3D input (got {x.dim()}D input)")
+            raise ValueError(f"Expected 2D or 3D input (got {x.dim()}D input)")

From 8970ed087a610989dc6e34d62679c5f90fad82f7 Mon Sep 17 00:00:00 2001
From: Antonin Raffin <antonin.raffin@ensta.org>
Date: Mon, 13 May 2024 10:09:38 +0200
Subject: [PATCH 10/32] Update documentation

---
 docs/guide/examples.rst | 13 +++++++++++++
 docs/misc/changelog.rst | 26 +++++++++++++++++++++++++-
 docs/modules/crossq.rst |  8 ++++----
 sb3_contrib/version.txt |  2 +-
 setup.py                |  6 ++++--
 5 files changed, 47 insertions(+), 8 deletions(-)

diff --git a/docs/guide/examples.rst b/docs/guide/examples.rst
index 83546cf6..5cd4d949 100644
--- a/docs/guide/examples.rst
+++ b/docs/guide/examples.rst
@@ -113,3 +113,16 @@ Train a PPO agent with a recurrent policy on the CartPole environment.
      obs, rewards, dones, info = vec_env.step(action)
      episode_starts = dones
      vec_env.render("human")
+
+ CrossQ
+ ------
+
+ Train a CrossQ agent on the Pendulum environment.
+
+ .. code-block:: python
+
+   from sb3_contrib import CrossQ
+
+   model = CrossQ("MlpPolicy", "Pendulum-v1", verbose=1, policy_kwargs=dict(net_arch=dict(pi=[256, 256], qf=[1024, 1024])))
+   model.learn(total_timesteps=5_000, log_interval=4)
+   model.save("crossq_pendulum")
diff --git a/docs/misc/changelog.rst b/docs/misc/changelog.rst
index 109b2eb5..f575792a 100644
--- a/docs/misc/changelog.rst
+++ b/docs/misc/changelog.rst
@@ -3,6 +3,30 @@
 Changelog
 ==========
 
+
+Release 2.4.0a0 (WIP)
+--------------------------
+
+Breaking Changes:
+^^^^^^^^^^^^^^^^^
+
+New Features:
+^^^^^^^^^^^^^
+- Added CrossQ (@danielpalen)
+
+Bug Fixes:
+^^^^^^^^^^
+
+Deprecations:
+^^^^^^^^^^^^^
+
+Others:
+^^^^^^^
+
+Documentation:
+^^^^^^^^^^^^^^
+
+
 Release 2.3.0 (2024-03-31)
 --------------------------
 
@@ -554,4 +578,4 @@ Contributors:
 -------------
 
 @ku2482 @guyk1971 @minhlong94 @ayeright @kronion @glmcdona @cyprienc @sgillen @Gregwar @rnederstigt @qgallouedec
-@mlodel @CppMaster @burakdmb @honglu2875 @ZikangXiong @AlexPasqua @jonasreiher @icheered @Armandpl
+@mlodel @CppMaster @burakdmb @honglu2875 @ZikangXiong @AlexPasqua @jonasreiher @icheered @Armandpl @danielpalen
diff --git a/docs/modules/crossq.rst b/docs/modules/crossq.rst
index e81aaa25..4e2e24da 100644
--- a/docs/modules/crossq.rst
+++ b/docs/modules/crossq.rst
@@ -21,6 +21,10 @@ This yield a simpler and more sample-efficient algorithm without requiring high
 
     MlpPolicy
 
+.. note::
+
+  Compared to the original implementation, the default network architecture for the q-value function is ``[1024, 1024]``
+  instead of ``[2048, 2048]``` as it provides a good compromise between speed and performance.
 
 Notes
 -----
@@ -53,9 +57,6 @@ Example
 
 .. code-block:: python
 
-  import gymnasium as gym
-  import numpy as np
-
   from sb3_contrib import CrossQ
 
   model = CrossQ("MlpPolicy", "Walker2d-v4")
@@ -96,4 +97,3 @@ CrossQ Policies
 .. autoclass:: sb3_contrib.crossq.policies.CrossQPolicy
   :members:
   :noindex:
-
diff --git a/sb3_contrib/version.txt b/sb3_contrib/version.txt
index 276cbf9e..e96f44fb 100644
--- a/sb3_contrib/version.txt
+++ b/sb3_contrib/version.txt
@@ -1 +1 @@
-2.3.0
+2.4.0a0
diff --git a/setup.py b/setup.py
index 49b24588..d2e02c07 100644
--- a/setup.py
+++ b/setup.py
@@ -30,11 +30,13 @@
 See documentation for the full list of included features.
 
 **RL Algorithms**:
-- [Truncated Quantile Critics (TQC)](https://arxiv.org/abs/2005.04269)
+- [Augmented Random Search (ARS)](https://arxiv.org/abs/1803.07055)
 - [Quantile Regression DQN (QR-DQN)](https://arxiv.org/abs/1710.10044)
 - [PPO with invalid action masking (MaskablePPO)](https://arxiv.org/abs/2006.14171)
+- [PPO with recurrent policy (RecurrentPPO aka PPO LSTM)](https://ppo-details.cleanrl.dev//2021/11/05/ppo-implementation-details/)
+- [Truncated Quantile Critics (TQC)](https://arxiv.org/abs/2005.04269)
 - [Trust Region Policy Optimization (TRPO)](https://arxiv.org/abs/1502.05477)
-- [Augmented Random Search (ARS)](https://arxiv.org/abs/1803.07055)
+- [Batch Normalization in Deep Reinforcement Learning (CrossQ)](https://openreview.net/forum?id=PczQtTsTIX)
 
 **Gym Wrappers**:
 - [Time Feature Wrapper](https://arxiv.org/abs/1712.00378)

From 8792621ab0732a44efd2f3e8fb5ae370442c614a Mon Sep 17 00:00:00 2001
From: Antonin Raffin <antonin.raffin@ensta.org>
Date: Mon, 13 May 2024 10:27:05 +0200
Subject: [PATCH 11/32] Rename to torch layers

---
 sb3_contrib/common/{network_layers.py => torch_layers.py} | 0
 sb3_contrib/crossq/policies.py                            | 2 +-
 2 files changed, 1 insertion(+), 1 deletion(-)
 rename sb3_contrib/common/{network_layers.py => torch_layers.py} (100%)

diff --git a/sb3_contrib/common/network_layers.py b/sb3_contrib/common/torch_layers.py
similarity index 100%
rename from sb3_contrib/common/network_layers.py
rename to sb3_contrib/common/torch_layers.py
diff --git a/sb3_contrib/crossq/policies.py b/sb3_contrib/crossq/policies.py
index dfd6cff0..17fe1820 100644
--- a/sb3_contrib/crossq/policies.py
+++ b/sb3_contrib/crossq/policies.py
@@ -14,7 +14,7 @@
 from stable_baselines3.common.type_aliases import PyTorchObs, Schedule
 from torch import nn
 
-from sb3_contrib.common.network_layers import BatchRenorm1d
+from sb3_contrib.common.torch_layers import BatchRenorm1d
 
 # CAP the standard deviation of the actor
 LOG_STD_MAX = 2

From 230a94889cae4481971b22e2af26b9ae1fd54154 Mon Sep 17 00:00:00 2001
From: Antonin Raffin <antonin.raffin@ensta.org>
Date: Mon, 13 May 2024 10:27:21 +0200
Subject: [PATCH 12/32] Fix for policy delay and minor edits

---
 sb3_contrib/crossq/crossq.py | 21 +++++++++++----------
 1 file changed, 11 insertions(+), 10 deletions(-)

diff --git a/sb3_contrib/crossq/crossq.py b/sb3_contrib/crossq/crossq.py
index b5a5fd0d..8db3b7a5 100644
--- a/sb3_contrib/crossq/crossq.py
+++ b/sb3_contrib/crossq/crossq.py
@@ -8,7 +8,6 @@
 from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
 from stable_baselines3.common.policies import BasePolicy
 from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
-from stable_baselines3.common.utils import get_parameters_by_name
 from torch.nn import functional as F
 
 from sb3_contrib.crossq.policies import Actor, CrossQCritic, CrossQPolicy, MlpPolicy
@@ -18,8 +17,8 @@
 
 class CrossQ(OffPolicyAlgorithm):
     """
-    Implementation of the CrossQ algorithm.
-    Paper: https://openreview.net/pdf?id=PczQtTsTIX
+    Implementation of Batch Normalization in Deep Reinforcement Learning (CrossQ).
+    Paper: https://openreview.net/forum?id=PczQtTsTIX
 
     :param policy: The policy model to use (MlpPolicy)
     :param env: The environment to learn from (if registered in Gym, can be str)
@@ -143,8 +142,6 @@ def __init__(
     def _setup_model(self) -> None:
         super()._setup_model()
         self._create_aliases()
-        # Running mean and running var
-        self.batch_norm_stats = get_parameters_by_name(self.critic, ["running_"])
         # Target entropy is used when learning the entropy coefficient
         if self.target_entropy == "auto":
             # automatically set target entropy if needed
@@ -193,6 +190,9 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
         actor_losses, critic_losses = [], []
 
         for _ in range(gradient_steps):
+            self._n_updates += 1
+            # delayed updates
+            update_actor_and_temperature = self._n_updates % self.policy_delay == 0
             # Sample replay buffer
             replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)  # type: ignore[union-attr]
 
@@ -224,7 +224,7 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
 
             # Optimize entropy coefficient, also called
             # entropy temperature or alpha in the paper
-            if ent_coef_loss is not None and self.ent_coef_optimizer is not None and self._n_updates % self.policy_delay == 0:
+            if ent_coef_loss is not None and self.ent_coef_optimizer is not None and update_actor_and_temperature:
 
                 self.ent_coef_optimizer.zero_grad()
                 ent_coef_loss.backward()
@@ -258,6 +258,7 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
             all_q_values = th.cat(self.critic(all_obs, all_acts), dim=1)
             self.critic.set_training_mode(False)
             current_q_values, next_q_values = th.split(all_q_values, batch_size, dim=0)
+            # (batch_size, n_critics) -> (n_critics, batch_size, 1)
             current_q_values = current_q_values.T[..., None]
 
             with th.no_grad():
@@ -280,7 +281,8 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
             critic_loss.backward()
             self.critic.optimizer.step()
 
-            if self._n_updates % self.policy_delay == 0:
+            # Delayed policy updates
+            if update_actor_and_temperature:
                 # Compute actor loss
                 self.critic.set_training_mode(False)
                 q_values_pi = th.cat(self.critic(replay_data.observations, actions_pi), dim=1)
@@ -295,11 +297,10 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
                 actor_loss.backward()
                 self.actor.optimizer.step()
 
-        self._n_updates += gradient_steps
-
         self.logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
         self.logger.record("train/ent_coef", np.mean(ent_coefs))
-        self.logger.record("train/actor_loss", np.mean(actor_losses))
+        if len(actor_losses) > 0:
+            self.logger.record("train/actor_loss", np.mean(actor_losses))
         self.logger.record("train/critic_loss", np.mean(critic_losses))
         if len(ent_coef_losses) > 0:
             self.logger.record("train/ent_coef_loss", np.mean(ent_coef_losses))

From cd8bd7dea1b244e8e8b3e169ab5548240f4d7df5 Mon Sep 17 00:00:00 2001
From: Antonin Raffin <antonin.raffin@ensta.org>
Date: Mon, 13 May 2024 10:27:37 +0200
Subject: [PATCH 13/32] Update tests

---
 tests/test_run.py | 19 +++++++------------
 1 file changed, 7 insertions(+), 12 deletions(-)

diff --git a/tests/test_run.py b/tests/test_run.py
index ed524f22..50e6ac3c 100644
--- a/tests/test_run.py
+++ b/tests/test_run.py
@@ -8,14 +8,9 @@
 from sb3_contrib.common.vec_env import AsyncEval
 
 
-def test():
-    model = CrossQ(
-        "MlpPolicy",
-        "Pendulum-v1",
-        learning_starts=100,
-        verbose=1,
-    )
-    model.learn(total_timesteps=300)
+def test_crossq():
+    model = CrossQ("MlpPolicy", "Pendulum-v1", learning_starts=100, verbose=1, policy_kwargs=dict(net_arch=[64]))
+    model.learn(total_timesteps=110)
 
 
 @pytest.mark.parametrize("ent_coef", ["auto", 0.01, "auto_0.01"])
@@ -28,7 +23,7 @@ def test_tqc(ent_coef):
         verbose=1,
         ent_coef=ent_coef,
     )
-    model.learn(total_timesteps=300, progress_bar=True)
+    model.learn(total_timesteps=110, progress_bar=True)
 
 
 @pytest.mark.parametrize("n_critics", [1, 3])
@@ -41,7 +36,7 @@ def test_n_critics(n_critics):
         learning_starts=100,
         verbose=1,
     )
-    model.learn(total_timesteps=300)
+    model.learn(total_timesteps=110)
 
 
 def test_sde():
@@ -53,7 +48,7 @@ def test_sde():
         learning_starts=100,
         verbose=1,
     )
-    model.learn(total_timesteps=300)
+    model.learn(total_timesteps=110)
     model.policy.reset_noise()
     model.policy.actor.get_std()
 
@@ -62,7 +57,7 @@ def test_qrdqn():
     model = QRDQN(
         "MlpPolicy",
         "CartPole-v1",
-        policy_kwargs=dict(n_quantiles=25, net_arch=[64, 64]),
+        policy_kwargs=dict(n_quantiles=25, net_arch=[64]),
         learning_starts=100,
         buffer_size=500,
         learning_rate=3e-4,

From 27a96f647234716558dc685c354efb242cbd7b18 Mon Sep 17 00:00:00 2001
From: Antonin Raffin <antonin.raffin@ensta.org>
Date: Mon, 13 May 2024 10:36:18 +0200
Subject: [PATCH 14/32] Update documentation

---
 docs/common/torch_layers.rst       |  7 +++++++
 docs/guide/examples.rst            | 16 ++++++++--------
 docs/index.rst                     |  1 +
 docs/modules/crossq.rst            |  5 +++--
 sb3_contrib/common/torch_layers.py | 20 ++++++++------------
 5 files changed, 27 insertions(+), 22 deletions(-)
 create mode 100644 docs/common/torch_layers.rst

diff --git a/docs/common/torch_layers.rst b/docs/common/torch_layers.rst
new file mode 100644
index 00000000..a6d39a27
--- /dev/null
+++ b/docs/common/torch_layers.rst
@@ -0,0 +1,7 @@
+.. _th_layers:
+
+Torch Layers
+============
+
+.. automodule:: sb3_contrib.common.torch_layers
+  :members:
diff --git a/docs/guide/examples.rst b/docs/guide/examples.rst
index 5cd4d949..ebe80446 100644
--- a/docs/guide/examples.rst
+++ b/docs/guide/examples.rst
@@ -114,15 +114,15 @@ Train a PPO agent with a recurrent policy on the CartPole environment.
      episode_starts = dones
      vec_env.render("human")
 
- CrossQ
- ------
+CrossQ
+------
 
- Train a CrossQ agent on the Pendulum environment.
+Train a CrossQ agent on the Pendulum environment.
 
- .. code-block:: python
+.. code-block:: python
 
-   from sb3_contrib import CrossQ
+ from sb3_contrib import CrossQ
 
-   model = CrossQ("MlpPolicy", "Pendulum-v1", verbose=1, policy_kwargs=dict(net_arch=dict(pi=[256, 256], qf=[1024, 1024])))
-   model.learn(total_timesteps=5_000, log_interval=4)
-   model.save("crossq_pendulum")
+ model = CrossQ("MlpPolicy", "Pendulum-v1", verbose=1, policy_kwargs=dict(net_arch=dict(pi=[256, 256], qf=[1024, 1024])))
+ model.learn(total_timesteps=5_000, log_interval=4)
+ model.save("crossq_pendulum")
diff --git a/docs/index.rst b/docs/index.rst
index 3e7c7a4e..ff32f1fe 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -43,6 +43,7 @@ RL Baselines3 Zoo also offers a simple interface to train, evaluate agents and d
   :maxdepth: 1
   :caption: Common
 
+  common/torch_layers
   common/utils
   common/wrappers
 
diff --git a/docs/modules/crossq.rst b/docs/modules/crossq.rst
index 4e2e24da..51c507c1 100644
--- a/docs/modules/crossq.rst
+++ b/docs/modules/crossq.rst
@@ -24,13 +24,14 @@ This yield a simpler and more sample-efficient algorithm without requiring high
 .. note::
 
   Compared to the original implementation, the default network architecture for the q-value function is ``[1024, 1024]``
-  instead of ``[2048, 2048]``` as it provides a good compromise between speed and performance.
+  instead of ``[2048, 2048]`` as it provides a good compromise between speed and performance.
 
 Notes
 -----
 
 - Original paper: https://openreview.net/pdf?id=PczQtTsTIX
 - Original Implementation: https://github.com/adityab/CrossQ
+- SBX Implementation: https://github.com/araffin/sbx
 
 
 Can I use?
@@ -68,7 +69,7 @@ Results
 -------
 
 Performance evaluation of CrossQ on six MuJoCo environments.
-Compared to results from the original paper as well as a version from SBX.
+Compared to results from the original paper as well as a version from `SBX <https://github.com/araffin/sbx>`_.
 
 .. image:: ../images/crossQ_performance.png
 
diff --git a/sb3_contrib/common/torch_layers.py b/sb3_contrib/common/torch_layers.py
index 03d2f192..64dec401 100644
--- a/sb3_contrib/common/torch_layers.py
+++ b/sb3_contrib/common/torch_layers.py
@@ -1,6 +1,6 @@
 import torch
 
-__all__ = ["BatchRenorm1d"]
+__all__ = ["BatchRenorm1d", "BatchRenorm"]
 
 
 class BatchRenorm(torch.jit.ScriptModule):
@@ -18,13 +18,12 @@ class BatchRenorm(torch.jit.ScriptModule):
     training mode. During evaluation mode, the running statistics are used for normalization but
     not updated.
 
-    Args:
-        num_features: Number of features in the input tensor.
-        eps: A value added to the variance for numerical stability.
-        momentum: The value used for the ra_mean and ra_var computation.
-        affine: A boolean value that when set to True, this module has learnable
+    :param num_features: Number of features in the input tensor.
+    :param eps: A value added to the variance for numerical stability.
+    :param momentum: The value used for the ra_mean and ra_var computation.
+    :param affine: A boolean value that when set to True, this module has learnable
             affine parameters. Default: True
-        warmup_steps: Number of warum steps that are performed before the running statistics
+    :param warmup_steps: Number of warum steps that are performed before the running statistics
             are used form normalization. During the warump phase, the batch statistics are used.
     """
 
@@ -59,11 +58,8 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         """
         Normalize the input tensor.
 
-        Args:
-            x: Input tensor
-
-        Returns:
-            Normalized tensor.
+        :param x: Input tensor
+        :return: Normalized tensor.
         """
 
         if self.training:

From 3927a70b262d32c755367379790aa6ed4b8f2c2d Mon Sep 17 00:00:00 2001
From: Antonin RAFFIN <antonin.raffin@ensta.org>
Date: Sat, 6 Jul 2024 19:15:14 +0200
Subject: [PATCH 15/32] Update doc

---
 docs/guide/algos.rst    |  1 +
 docs/misc/changelog.rst |  5 +++--
 docs/modules/crossq.rst | 38 +++++++++++++++++++++++++++++++++++---
 3 files changed, 39 insertions(+), 5 deletions(-)

diff --git a/docs/guide/algos.rst b/docs/guide/algos.rst
index 8181382e..48e8fa4f 100644
--- a/docs/guide/algos.rst
+++ b/docs/guide/algos.rst
@@ -10,6 +10,7 @@ Name         ``Box``     ``Discrete`` ``MultiDiscrete`` ``MultiBinary`` Multi Pr
 ============ =========== ============ ================= =============== ================
 ARS          ✔️          ❌️            ❌                ❌                ✔️
 MaskablePPO  ❌           ✔️             ✔️                ✔️               ✔️
+CrossQ       ✔️          ❌            ❌                ❌                ✔️
 QR-DQN       ️❌          ️✔️            ❌                ❌                ✔️
 RecurrentPPO ✔️           ✔️             ✔️                ✔️               ✔️
 TQC          ✔️          ❌            ❌                ❌                ✔️
diff --git a/docs/misc/changelog.rst b/docs/misc/changelog.rst
index 5dd41838..c435fc48 100644
--- a/docs/misc/changelog.rst
+++ b/docs/misc/changelog.rst
@@ -3,7 +3,7 @@
 Changelog
 ==========
 
-Release 2.4.0a4 (WIP)
+Release 2.4.0a5 (WIP)
 --------------------------
 
 Breaking Changes:
@@ -12,7 +12,8 @@ Breaking Changes:
 
 New Features:
 ^^^^^^^^^^^^^
-- Added CrossQ (@danielpalen)
+- Added ``CrossQ`` algorithm, from "Batch Normalization in Deep Reinforcement Learning" paper (@danielpalen)
+- Added ``BatchRenorm`` PyTorch layer used in ``CrossQ`` (@danielpalen)
 
 Bug Fixes:
 ^^^^^^^^^^
diff --git a/docs/modules/crossq.rst b/docs/modules/crossq.rst
index 51c507c1..719f5ec4 100644
--- a/docs/modules/crossq.rst
+++ b/docs/modules/crossq.rst
@@ -25,13 +25,19 @@ This yield a simpler and more sample-efficient algorithm without requiring high
 
   Compared to the original implementation, the default network architecture for the q-value function is ``[1024, 1024]``
   instead of ``[2048, 2048]`` as it provides a good compromise between speed and performance.
+  We also use the default hyperparameters for Adam, since they have little impact on performance.
+
+.. note::
+
+  There is currently no ``CnnPolicy`` for using CrossQ with images. We welcome help from contributors to add this feature.
+
 
 Notes
 -----
 
 - Original paper: https://openreview.net/pdf?id=PczQtTsTIX
 - Original Implementation: https://github.com/adityab/CrossQ
-- SBX Implementation: https://github.com/araffin/sbx
+- SBX (SB3 Jax) Implementation: https://github.com/araffin/sbx
 
 
 Can I use?
@@ -49,7 +55,7 @@ Discrete      ❌      ✔️
 Box           ✔️      ✔️
 MultiDiscrete ❌      ✔️
 MultiBinary   ❌      ✔️
-Dict          ❌      ✔️
+Dict          ❌      ❌
 ============= ====== ===========
 
 
@@ -68,11 +74,37 @@ Example
 Results
 -------
 
-Performance evaluation of CrossQ on six MuJoCo environments.
+Performance evaluation of CrossQ on six MuJoCo environments, see `PR #243 <https://github.com/Stable-Baselines-Team/stable-baselines3-contrib/pull/243>`_.
 Compared to results from the original paper as well as a version from `SBX <https://github.com/araffin/sbx>`_.
 
 .. image:: ../images/crossQ_performance.png
 
+
+How to replicate the results?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Clone RL-Zoo:
+
+.. code-block:: bash
+
+  git clone https://github.com/DLR-RM/rl-baselines3-zoo
+  cd rl-baselines3-zoo/
+
+Run the benchmark (replace ``$ENV_ID`` by the envs mentioned above):
+
+.. code-block:: bash
+
+  python train.py --algo crossq --env $ENV_ID --n-eval-envs 5 --eval-episodes 20 --eval-freq 25000
+
+
+Plot the results:
+
+.. code-block:: bash
+
+  python scripts/all_plots.py -a crossq -e HalfCheetah Ant Hopper Walker2D -f logs/ -o logs/crossq_results
+  python scripts/plot_from_file.py -i logs/crossq_results.pkl -latex -l CrossQ
+
+
 Comments
 --------
 

From 2019327dce57986af71e4a6ac9d94d79fe5446be Mon Sep 17 00:00:00 2001
From: Antonin RAFFIN <antonin.raffin@ensta.org>
Date: Sat, 6 Jul 2024 19:15:24 +0200
Subject: [PATCH 16/32] Add more tests for crossQ

---
 tests/test_run.py       | 15 +++++++++++----
 tests/test_save_load.py | 21 ++++++++++++++-------
 2 files changed, 25 insertions(+), 11 deletions(-)

diff --git a/tests/test_run.py b/tests/test_run.py
index 50e6ac3c..b31ed445 100644
--- a/tests/test_run.py
+++ b/tests/test_run.py
@@ -9,7 +9,13 @@
 
 
 def test_crossq():
-    model = CrossQ("MlpPolicy", "Pendulum-v1", learning_starts=100, verbose=1, policy_kwargs=dict(net_arch=[64]))
+    model = CrossQ(
+        "MlpPolicy",
+        "Pendulum-v1",
+        learning_starts=100,
+        verbose=1,
+        policy_kwargs=dict(net_arch=[32], renorm_warmup_steps=1),
+    )
     model.learn(total_timesteps=110)
 
 
@@ -39,11 +45,12 @@ def test_n_critics(n_critics):
     model.learn(total_timesteps=110)
 
 
-def test_sde():
-    model = TQC(
+@pytest.mark.parametrize("model_class", [TQC, CrossQ])
+def test_sde(model_class):
+    model = model_class(
         "MlpPolicy",
         "Pendulum-v1",
-        policy_kwargs=dict(net_arch=[64]),
+        policy_kwargs=dict(net_arch=[16]),
         use_sde=True,
         learning_starts=100,
         verbose=1,
diff --git a/tests/test_save_load.py b/tests/test_save_load.py
index 502d2394..693169ea 100644
--- a/tests/test_save_load.py
+++ b/tests/test_save_load.py
@@ -12,9 +12,16 @@
 from stable_baselines3.common.utils import get_device
 from stable_baselines3.common.vec_env import DummyVecEnv
 
-from sb3_contrib import ARS, QRDQN, TQC, TRPO
+from sb3_contrib import ARS, QRDQN, TQC, TRPO, CrossQ
 
-MODEL_LIST = [ARS, QRDQN, TQC, TRPO]
+MODEL_LIST = [ARS, QRDQN, TQC, TRPO, CrossQ]
+
+
+def rand_like(tensor: th.Tensor) -> th.Tensor:
+    if th.is_floating_point(tensor):
+        return th.rand_like(tensor)
+    # int tensor
+    return th.randint_like(tensor, high=10)
 
 
 def select_env(model_class: BaseAlgorithm) -> gym.Env:
@@ -93,7 +100,7 @@ def test_save_load(tmp_path, model_class):
         else:
             # Again, skip the last item in state-dict
             random_params[object_name] = OrderedDict(
-                (param_name, th.rand_like(param)) for param_name, param in list(params.items())[:-1]
+                (param_name, rand_like(param)) for param_name, param in list(params.items())[:-1]
             )
 
     # Update model parameters with the new random values
@@ -267,8 +274,8 @@ def test_save_load_policy(tmp_path, model_class, policy_str):
     """
     kwargs = dict(policy_kwargs=dict(net_arch=[16]))
 
-    if policy_str == "CnnPolicy" and model_class is ARS:
-        pytest.skip("ARS does not support CnnPolicy")
+    if policy_str == "CnnPolicy" and model_class in [ARS, CrossQ]:
+        pytest.skip(f"{model_class.__name__} does not support CnnPolicy")
 
     if policy_str == "MlpPolicy":
         env = select_env(model_class)
@@ -312,7 +319,7 @@ def test_save_load_policy(tmp_path, model_class, policy_str):
     params = deepcopy(policy.state_dict())
 
     # Modify all parameters to be random values
-    random_params = {param_name: th.rand_like(param) for param_name, param in params.items()}
+    random_params = {param_name: rand_like(param) for param_name, param in params.items()}
 
     # Update model parameters with the new random values
     policy.load_state_dict(random_params)
@@ -409,7 +416,7 @@ def test_save_load_q_net(tmp_path, model_class, policy_str):
     params = deepcopy(q_net.state_dict())
 
     # Modify all parameters to be random values
-    random_params = {param_name: th.rand_like(param) for param_name, param in params.items()}
+    random_params = {param_name: rand_like(param) for param_name, param in params.items()}
 
     # Update model parameters with the new random values
     q_net.load_state_dict(random_params)

From b0213ec3b6eb3da6963463a152e4527e30fa6cdb Mon Sep 17 00:00:00 2001
From: Antonin RAFFIN <antonin.raffin@ensta.org>
Date: Sat, 6 Jul 2024 19:15:43 +0200
Subject: [PATCH 17/32] Improve doc and expose batchnorm params

---
 sb3_contrib/common/torch_layers.py | 19 +++++++-----
 sb3_contrib/crossq/policies.py     | 49 +++++++++++++++++++++++++++---
 sb3_contrib/version.txt            |  2 +-
 3 files changed, 56 insertions(+), 14 deletions(-)

diff --git a/sb3_contrib/common/torch_layers.py b/sb3_contrib/common/torch_layers.py
index 64dec401..9a73eddf 100644
--- a/sb3_contrib/common/torch_layers.py
+++ b/sb3_contrib/common/torch_layers.py
@@ -6,7 +6,8 @@
 class BatchRenorm(torch.jit.ScriptModule):
     """
     BatchRenorm Module (https://arxiv.org/abs/1702.03275).
-    Adapted to Pytorch from sbx.sbx.common.jax_layers.BatchRenorm
+    Adapted to Pytorch from
+    https://github.com/araffin/sbx/blob/master/sbx/common/jax_layers.py
 
     BatchRenorm is an improved version of vanilla BatchNorm. Contrary to BatchNorm,
     BatchRenorm uses the running statistics for normalizing the batches after a warmup phase.
@@ -20,11 +21,12 @@ class BatchRenorm(torch.jit.ScriptModule):
 
     :param num_features: Number of features in the input tensor.
     :param eps: A value added to the variance for numerical stability.
-    :param momentum: The value used for the ra_mean and ra_var computation.
+    :param momentum: The value used for the ra_mean and ra_var (running average) computation.
+        It controls the rate of convergence for the batch renormalization statistics.
     :param affine: A boolean value that when set to True, this module has learnable
             affine parameters. Default: True
     :param warmup_steps: Number of warum steps that are performed before the running statistics
-            are used form normalization. During the warump phase, the batch statistics are used.
+            are used for normalization. During the warump phase, the batch statistics are used.
     """
 
     def __init__(
@@ -47,6 +49,7 @@ def __init__(
         self.eps = eps
         self.step = 0
         self.momentum = momentum
+        # Clip scale and bias of the affine transform
         self.rmax = 3.0
         self.dmax = 5.0
         self.warmup_steps = warmup_steps
@@ -63,8 +66,8 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         """
 
         if self.training:
-            batch_mean = x.mean(0)
-            batch_var = x.var(0)
+            batch_mean = x.mean(dim=0)
+            batch_var = x.var(dim=0)
             batch_std = (batch_var + self.eps).sqrt()
 
             # Use batch statistics during initial warm up phase.
@@ -82,9 +85,6 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
                 d = d.clamp(-self.dmax, self.dmax)
 
                 # BatchNorm normalization, using minibatch stats and running average stats
-                # Because we use _normalize, this is equivalent to
-                # ((x - x_mean) / sigma) * r + d = ((x - x_mean) * r + d * sigma) / sigma
-                # where sigma = sqrt(var)
                 custom_mean = batch_mean - d * batch_var.sqrt() / r
                 custom_var = batch_var / (r**2)
 
@@ -107,6 +107,9 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
         return x
 
+    # def extra_repr(self) -> str:
+    #     return f"num_features={self.num_features}, momentum={self.momentum}, warmup_steps={self.warmup_steps}"
+
 
 class BatchRenorm1d(BatchRenorm):
     def _check_input_dim(self, x: torch.Tensor) -> None:
diff --git a/sb3_contrib/crossq/policies.py b/sb3_contrib/crossq/policies.py
index 17fe1820..1bc3e404 100644
--- a/sb3_contrib/crossq/policies.py
+++ b/sb3_contrib/crossq/policies.py
@@ -43,6 +43,11 @@ class Actor(BasePolicy):
     :param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.
     :param normalize_images: Whether to normalize images or not,
          dividing by 255.0 (True by default)
+    :param batch_norm: Whether to use Batch Renorm layers (default=True)
+    :param batch_norm_momentum: The rate of convergence for the batch renormalization statistics
+    :param batch_norm_eps: A small value added to the variance to prevent division by zero
+    :param renorm_warmup_steps: Number of steps to warm up BatchRenorm statistics before the running statistics
+            are used for normalization.
     """
 
     action_space: spaces.Box
@@ -64,6 +69,7 @@ def __init__(
         batch_norm: bool = True,
         batch_norm_momentum: float = 0.01,
         batch_norm_eps: float = 0.001,
+        renorm_warmup_steps: int = 100_000,
     ):
         super().__init__(
             observation_space,
@@ -87,14 +93,20 @@ def __init__(
         action_dim = get_action_dim(self.action_space)
         latent_pi_net = create_mlp(features_dim, -1, net_arch, activation_fn)
 
+        batch_norm_params = {
+            "momentum": batch_norm_momentum,
+            "eps": batch_norm_eps,
+            "warmup_steps": renorm_warmup_steps,
+        }
+
         if batch_norm:
             # If batchnorm, then we want to add torch.nn.Batch_Norm layers before every linear layer
             net: List[Union[nn.Module, BatchRenorm1d]] = []
             for layer in latent_pi_net:
                 if isinstance(layer, nn.Linear):
-                    net.append(BatchRenorm1d(layer.in_features, eps=batch_norm_eps, momentum=batch_norm_momentum))
+                    net.append(BatchRenorm1d(layer.in_features, **batch_norm_params))
                 net.append(layer)
-            net.append(BatchRenorm1d(net_arch[-1], eps=batch_norm_eps, momentum=batch_norm_momentum))
+            net.append(BatchRenorm1d(net_arch[-1], **batch_norm_params))
             latent_pi_net = net
 
         self.latent_pi = nn.Sequential(*latent_pi_net)
@@ -212,6 +224,11 @@ class CrossQCritic(BaseModel):
     :param n_critics: Number of critic networks to create.
     :param share_features_extractor: Whether the features extractor is shared or not
         between the actor and the critic (this saves computation time)
+    :param batch_norm: Whether to use Batch Renorm layers (default=True)
+    :param batch_norm_momentum: The rate of convergence for the batch renormalization statistics
+    :param batch_norm_eps: A small value added to the variance to prevent division by zero
+    :param renorm_warmup_steps: Number of steps to warm up BatchRenorm statistics before the running statistics
+            are used for normalization.
     """
 
     features_extractor: BaseFeaturesExtractor
@@ -230,6 +247,7 @@ def __init__(
         batch_norm: bool = True,
         batch_norm_momentum: float = 0.01,
         batch_norm_eps: float = 0.001,
+        renorm_warmup_steps: int = 100_000,
     ):
         super().__init__(
             observation_space,
@@ -239,6 +257,11 @@ def __init__(
         )
 
         action_dim = get_action_dim(self.action_space)
+        batch_norm_params = {
+            "momentum": batch_norm_momentum,
+            "eps": batch_norm_eps,
+            "warmup_steps": renorm_warmup_steps,
+        }
 
         self.share_features_extractor = share_features_extractor
         self.n_critics = n_critics
@@ -251,7 +274,7 @@ def __init__(
                 net: List[Union[nn.Module, BatchRenorm1d]] = []
                 for layer in q_net_list:
                     if isinstance(layer, nn.Linear):
-                        net.append(BatchRenorm1d(layer.in_features, eps=batch_norm_eps, momentum=batch_norm_momentum))
+                        net.append(BatchRenorm1d(layer.in_features, **batch_norm_params))
                     net.append(layer)
                 q_net_list = net
 
@@ -277,6 +300,11 @@ class CrossQPolicy(BasePolicy):
     :param lr_schedule: Learning rate schedule (could be constant)
     :param net_arch: The specification of the policy and value networks.
     :param activation_fn: Activation function
+    :param batch_norm: Whether to use Batch Renorm layers (default=True)
+    :param batch_norm_momentum: The rate of convergence for the batch renormalization statistics
+    :param batch_norm_eps: A small value added to the variance to prevent division by zero
+    :param renorm_warmup_steps: Number of steps to warm up BatchRenorm statistics before the running statistics
+            are used for normalization.
     :param use_sde: Whether to use State Dependent Exploration or not
     :param log_std_init: Initial value for the log standard deviation
     :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure
@@ -307,6 +335,10 @@ def __init__(
         lr_schedule: Schedule,
         net_arch: Optional[Union[List[int], Dict[str, List[int]]]] = None,
         activation_fn: Type[nn.Module] = nn.ReLU,
+        batch_norm: bool = True,
+        batch_norm_momentum: float = 0.99,
+        batch_norm_eps: float = 0.001,
+        renorm_warmup_steps: int = 100_000,
         use_sde: bool = False,
         log_std_init: float = -3,
         use_expln: bool = False,
@@ -338,6 +370,13 @@ def __init__(
 
         actor_arch, critic_arch = get_actor_critic_arch(net_arch)
 
+        self.batch_norm_params = {
+            "batch_norm": batch_norm,
+            "batch_norm_momentum": batch_norm_momentum,
+            "batch_norm_eps": batch_norm_eps,
+            "renorm_warmup_steps": renorm_warmup_steps,
+        }
+
         self.net_arch = net_arch
         self.activation_fn = activation_fn
         self.net_args = {
@@ -346,7 +385,7 @@ def __init__(
             "net_arch": actor_arch,
             "activation_fn": self.activation_fn,
             "normalize_images": normalize_images,
-            "batch_norm": True,
+            **self.batch_norm_params,
         }
         self.actor_kwargs = self.net_args.copy()
 
@@ -363,7 +402,6 @@ def __init__(
                 "n_critics": n_critics,
                 "net_arch": critic_arch,
                 "share_features_extractor": share_features_extractor,
-                "batch_norm": True,
             }
         )
 
@@ -413,6 +451,7 @@ def _get_constructor_parameters(self) -> Dict[str, Any]:
                 optimizer_kwargs=self.optimizer_kwargs,
                 features_extractor_class=self.features_extractor_class,
                 features_extractor_kwargs=self.features_extractor_kwargs,
+                **self.batch_norm_params,
             )
         )
         return data
diff --git a/sb3_contrib/version.txt b/sb3_contrib/version.txt
index 3ebd20b1..a1fd35b5 100644
--- a/sb3_contrib/version.txt
+++ b/sb3_contrib/version.txt
@@ -1 +1 @@
-2.4.0a4
\ No newline at end of file
+2.4.0a5

From 454224d088f9fc79e5f4d4dec1cb38473daf6342 Mon Sep 17 00:00:00 2001
From: Antonin RAFFIN <antonin.raffin@ensta.org>
Date: Sat, 6 Jul 2024 19:36:31 +0200
Subject: [PATCH 18/32] Add some comments and todos and fix type check

---
 sb3_contrib/crossq/crossq.py   | 17 +++++++++--------
 sb3_contrib/crossq/policies.py |  8 +++++---
 2 files changed, 14 insertions(+), 11 deletions(-)

diff --git a/sb3_contrib/crossq/crossq.py b/sb3_contrib/crossq/crossq.py
index 8db3b7a5..bec161d8 100644
--- a/sb3_contrib/crossq/crossq.py
+++ b/sb3_contrib/crossq/crossq.py
@@ -66,6 +66,7 @@ class CrossQ(OffPolicyAlgorithm):
 
     policy_aliases: ClassVar[Dict[str, Type[BasePolicy]]] = {
         "MlpPolicy": MlpPolicy,
+        # TODO: Implement CnnPolicy and MultiInputPolicy
     }
     policy: CrossQPolicy
     actor: Actor
@@ -107,7 +108,7 @@ def __init__(
             buffer_size,
             learning_starts,
             batch_size,
-            1.0,
+            1.0,  # no target networks, tau=1.0
             gamma,
             train_freq,
             gradient_steps,
@@ -191,7 +192,7 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
 
         for _ in range(gradient_steps):
             self._n_updates += 1
-            # delayed updates
+            # Delayed updates
             update_actor_and_temperature = self._n_updates % self.policy_delay == 0
             # Sample replay buffer
             replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)  # type: ignore[union-attr]
@@ -204,6 +205,8 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
             # of actor and critic carefully. This is because of the BatchNorm layers in the networks
             # which behave differently in train and eval modes.
 
+            # TODO: replace with more precise, set_training_mode_bn(), to allow the use of Dropout
+            # TODO: double check, should be moved with the ent coef to the delayed update?
             self.actor.set_training_mode(True)
             actions_pi, log_prob = self.actor.action_log_prob(replay_data.observations)
             log_prob = log_prob.reshape(-1, 1)
@@ -252,10 +255,10 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
             # 2. From a computational perspective a single forward pass is simply more efficient than
             #    two sequential forward passes.
             all_obs = th.cat([replay_data.observations, replay_data.next_observations], dim=0)
-            all_acts = th.cat([replay_data.actions, next_actions], dim=0)
+            all_actions = th.cat([replay_data.actions, next_actions], dim=0)
 
             self.critic.set_training_mode(True)
-            all_q_values = th.cat(self.critic(all_obs, all_acts), dim=1)
+            all_q_values = th.cat(self.critic(all_obs, all_actions), dim=1)
             self.critic.set_training_mode(False)
             current_q_values, next_q_values = th.split(all_q_values, batch_size, dim=0)
             # (batch_size, n_critics) -> (n_critics, batch_size, 1)
@@ -263,10 +266,8 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
 
             with th.no_grad():
                 # Compute the target Q value
-                next_q_values = next_q_values.detach()
-                next_q_values, _ = th.min(next_q_values, dim=1, keepdim=True)
-
-                # add entropy term
+                next_q_values, _ = th.min(next_q_values.detach(), dim=1, keepdim=True)
+                # Add entropy term
                 next_q_values = next_q_values - ent_coef * next_log_prob.reshape(-1, 1)
                 # td error + entropy term
                 target_q_values = replay_data.rewards + (1 - replay_data.dones) * self.gamma * next_q_values
diff --git a/sb3_contrib/crossq/policies.py b/sb3_contrib/crossq/policies.py
index 1bc3e404..85797072 100644
--- a/sb3_contrib/crossq/policies.py
+++ b/sb3_contrib/crossq/policies.py
@@ -99,14 +99,15 @@ def __init__(
             "warmup_steps": renorm_warmup_steps,
         }
 
+        # TODO(antonin): refactor once we can have batch norm/custom layers easily in SB3
         if batch_norm:
             # If batchnorm, then we want to add torch.nn.Batch_Norm layers before every linear layer
             net: List[Union[nn.Module, BatchRenorm1d]] = []
             for layer in latent_pi_net:
                 if isinstance(layer, nn.Linear):
-                    net.append(BatchRenorm1d(layer.in_features, **batch_norm_params))
+                    net.append(BatchRenorm1d(layer.in_features, **batch_norm_params))  # type: ignore[arg-type]
                 net.append(layer)
-            net.append(BatchRenorm1d(net_arch[-1], **batch_norm_params))
+            net.append(BatchRenorm1d(net_arch[-1], **batch_norm_params))  # type: ignore[arg-type]
             latent_pi_net = net
 
         self.latent_pi = nn.Sequential(*latent_pi_net)
@@ -269,12 +270,13 @@ def __init__(
         for idx in range(n_critics):
             q_net_list = create_mlp(features_dim + action_dim, 1, net_arch, activation_fn)
 
+            # TODO(antonin): refactor once we can have batch norm/custom layers easily in SB3
             if batch_norm:
                 # If batchnorm, then we want to add torch.nn.Batch_Norm layers before every linear layer
                 net: List[Union[nn.Module, BatchRenorm1d]] = []
                 for layer in q_net_list:
                     if isinstance(layer, nn.Linear):
-                        net.append(BatchRenorm1d(layer.in_features, **batch_norm_params))
+                        net.append(BatchRenorm1d(layer.in_features, **batch_norm_params))  # type: ignore[arg-type]
                     net.append(layer)
                 q_net_list = net
 

From bbd654caf9e4c54467210ab146081ef6a612e6b3 Mon Sep 17 00:00:00 2001
From: Antonin RAFFIN <antonin.raffin@ensta.org>
Date: Fri, 19 Jul 2024 14:28:59 +0200
Subject: [PATCH 19/32] Use torch module for BN

---
 sb3_contrib/common/torch_layers.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/sb3_contrib/common/torch_layers.py b/sb3_contrib/common/torch_layers.py
index 9a73eddf..9927cd6a 100644
--- a/sb3_contrib/common/torch_layers.py
+++ b/sb3_contrib/common/torch_layers.py
@@ -3,7 +3,7 @@
 __all__ = ["BatchRenorm1d", "BatchRenorm"]
 
 
-class BatchRenorm(torch.jit.ScriptModule):
+class BatchRenorm(torch.nn.Module):
     """
     BatchRenorm Module (https://arxiv.org/abs/1702.03275).
     Adapted to Pytorch from
@@ -49,6 +49,7 @@ def __init__(
         self.eps = eps
         self.step = 0
         self.momentum = momentum
+        self.num_features = num_features
         # Clip scale and bias of the affine transform
         self.rmax = 3.0
         self.dmax = 5.0
@@ -107,8 +108,11 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
         return x
 
-    # def extra_repr(self) -> str:
-    #     return f"num_features={self.num_features}, momentum={self.momentum}, warmup_steps={self.warmup_steps}"
+    def extra_repr(self) -> str:
+        return (
+            f"num_features={self.num_features}, momentum={self.momentum}, "
+            f"warmup_steps={self.warmup_steps}, affine={self.affine}"
+        )
 
 
 class BatchRenorm1d(BatchRenorm):

From bb80218b1730c88bea3e18b362bb17811dc0f2d3 Mon Sep 17 00:00:00 2001
From: Antonin RAFFIN <antonin.raffin@ensta.org>
Date: Fri, 19 Jul 2024 14:29:33 +0200
Subject: [PATCH 20/32] Re-organize losses

---
 sb3_contrib/crossq/crossq.py | 44 ++++++++++++++++--------------------
 1 file changed, 19 insertions(+), 25 deletions(-)

diff --git a/sb3_contrib/crossq/crossq.py b/sb3_contrib/crossq/crossq.py
index bec161d8..9c754e4e 100644
--- a/sb3_contrib/crossq/crossq.py
+++ b/sb3_contrib/crossq/crossq.py
@@ -192,8 +192,6 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
 
         for _ in range(gradient_steps):
             self._n_updates += 1
-            # Delayed updates
-            update_actor_and_temperature = self._n_updates % self.policy_delay == 0
             # Sample replay buffer
             replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)  # type: ignore[union-attr]
 
@@ -205,39 +203,21 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
             # of actor and critic carefully. This is because of the BatchNorm layers in the networks
             # which behave differently in train and eval modes.
 
-            # TODO: replace with more precise, set_training_mode_bn(), to allow the use of Dropout
-            # TODO: double check, should be moved with the ent coef to the delayed update?
-            self.actor.set_training_mode(True)
-            actions_pi, log_prob = self.actor.action_log_prob(replay_data.observations)
-            log_prob = log_prob.reshape(-1, 1)
-            self.actor.set_training_mode(False)
-
-            ent_coef_loss = None
-            if self.ent_coef_optimizer is not None and self.log_ent_coef is not None:
+            if self.log_ent_coef is not None:
                 # Important: detach the variable from the graph
                 # so we don't change it with other losses
                 # see https://github.com/rail-berkeley/softlearning/issues/60
                 ent_coef = th.exp(self.log_ent_coef.detach())
-                ent_coef_loss = -(self.log_ent_coef * (log_prob + self.target_entropy).detach()).mean()
-                ent_coef_losses.append(ent_coef_loss.item())
             else:
                 ent_coef = self.ent_coef_tensor
 
             ent_coefs.append(ent_coef.item())
 
-            # Optimize entropy coefficient, also called
-            # entropy temperature or alpha in the paper
-            if ent_coef_loss is not None and self.ent_coef_optimizer is not None and update_actor_and_temperature:
-
-                self.ent_coef_optimizer.zero_grad()
-                ent_coef_loss.backward()
-                self.ent_coef_optimizer.step()
-
             with th.no_grad():
                 # Select action according to policy
+                # TODO: replace with more precise, set_training_mode_bn(), to allow the use of Dropout
                 self.actor.set_training_mode(False)
                 next_actions, next_log_prob = self.actor.action_log_prob(replay_data.next_observations)
-                self.actor.set_training_mode(False)
 
             # Joint forward pass of obs/next_obs and actions/next_state_actions to have only
             # one forward pass with shape (n_critics, 2 * batch_size, 1).
@@ -256,7 +236,7 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
             #    two sequential forward passes.
             all_obs = th.cat([replay_data.observations, replay_data.next_observations], dim=0)
             all_actions = th.cat([replay_data.actions, next_actions], dim=0)
-
+            # Update critic BN stats
             self.critic.set_training_mode(True)
             all_q_values = th.cat(self.critic(all_obs, all_actions), dim=1)
             self.critic.set_training_mode(False)
@@ -283,11 +263,25 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
             self.critic.optimizer.step()
 
             # Delayed policy updates
-            if update_actor_and_temperature:
+            if self._n_updates % self.policy_delay == 0:
+                # Sample action according to policy and update actor BN stats
+                self.actor.set_training_mode(True)
+                actions_pi, log_prob = self.actor.action_log_prob(replay_data.observations)
+                log_prob = log_prob.reshape(-1, 1)
+                self.actor.set_training_mode(False)
+
+                # Optimize entropy coefficient, also called entropy temperature or alpha in the paper
+                if self.ent_coef_optimizer is not None:
+                    ent_coef_loss = -(self.log_ent_coef * (log_prob + self.target_entropy).detach()).mean()
+                    ent_coef_losses.append(ent_coef_loss.item())
+
+                    self.ent_coef_optimizer.zero_grad()
+                    ent_coef_loss.backward()
+                    self.ent_coef_optimizer.step()
+
                 # Compute actor loss
                 self.critic.set_training_mode(False)
                 q_values_pi = th.cat(self.critic(replay_data.observations, actions_pi), dim=1)
-                self.critic.set_training_mode(False)
 
                 min_qf_pi, _ = th.min(q_values_pi, dim=1, keepdim=True)
                 actor_loss = (ent_coef * log_prob.reshape(-1, 1) - min_qf_pi).mean()

From a717d13e4e6cca1380c176ab24d833fc45612d20 Mon Sep 17 00:00:00 2001
From: Antonin RAFFIN <antonin.raffin@ensta.org>
Date: Fri, 19 Jul 2024 14:46:36 +0200
Subject: [PATCH 21/32] Add set_bn_training_mode

---
 sb3_contrib/common/torch_layers.py |  2 ++
 sb3_contrib/crossq/crossq.py       | 14 +++++++-------
 sb3_contrib/crossq/policies.py     | 22 ++++++++++++++++++++++
 3 files changed, 31 insertions(+), 7 deletions(-)

diff --git a/sb3_contrib/common/torch_layers.py b/sb3_contrib/common/torch_layers.py
index 9927cd6a..76a93bb7 100644
--- a/sb3_contrib/common/torch_layers.py
+++ b/sb3_contrib/common/torch_layers.py
@@ -67,6 +67,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         """
 
         if self.training:
+            # Compute batch statistics
             batch_mean = x.mean(dim=0)
             batch_var = x.var(dim=0)
             batch_std = (batch_var + self.eps).sqrt()
@@ -98,6 +99,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
             self.steps += 1
 
         else:
+            # Use running statistics during evaluation mode
             custom_mean, custom_var = self.ra_mean, self.ra_var
 
         # Normalize
diff --git a/sb3_contrib/crossq/crossq.py b/sb3_contrib/crossq/crossq.py
index 9c754e4e..2a744cfd 100644
--- a/sb3_contrib/crossq/crossq.py
+++ b/sb3_contrib/crossq/crossq.py
@@ -215,8 +215,8 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
 
             with th.no_grad():
                 # Select action according to policy
-                # TODO: replace with more precise, set_training_mode_bn(), to allow the use of Dropout
-                self.actor.set_training_mode(False)
+                # Use more precise set_training_mode to allow the use of Dropout
+                self.actor.set_bn_training_mode(False)
                 next_actions, next_log_prob = self.actor.action_log_prob(replay_data.next_observations)
 
             # Joint forward pass of obs/next_obs and actions/next_state_actions to have only
@@ -237,9 +237,9 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
             all_obs = th.cat([replay_data.observations, replay_data.next_observations], dim=0)
             all_actions = th.cat([replay_data.actions, next_actions], dim=0)
             # Update critic BN stats
-            self.critic.set_training_mode(True)
+            self.critic.set_bn_training_mode(True)
             all_q_values = th.cat(self.critic(all_obs, all_actions), dim=1)
-            self.critic.set_training_mode(False)
+            self.critic.set_bn_training_mode(False)
             current_q_values, next_q_values = th.split(all_q_values, batch_size, dim=0)
             # (batch_size, n_critics) -> (n_critics, batch_size, 1)
             current_q_values = current_q_values.T[..., None]
@@ -265,10 +265,10 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
             # Delayed policy updates
             if self._n_updates % self.policy_delay == 0:
                 # Sample action according to policy and update actor BN stats
-                self.actor.set_training_mode(True)
+                self.actor.set_bn_training_mode(True)
                 actions_pi, log_prob = self.actor.action_log_prob(replay_data.observations)
                 log_prob = log_prob.reshape(-1, 1)
-                self.actor.set_training_mode(False)
+                self.actor.set_bn_training_mode(False)
 
                 # Optimize entropy coefficient, also called entropy temperature or alpha in the paper
                 if self.ent_coef_optimizer is not None:
@@ -280,7 +280,7 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
                     self.ent_coef_optimizer.step()
 
                 # Compute actor loss
-                self.critic.set_training_mode(False)
+                self.critic.set_bn_training_mode(False)
                 q_values_pi = th.cat(self.critic(replay_data.observations, actions_pi), dim=1)
 
                 min_qf_pi, _ = th.min(q_values_pi, dim=1, keepdim=True)
diff --git a/sb3_contrib/crossq/policies.py b/sb3_contrib/crossq/policies.py
index 85797072..7a585a58 100644
--- a/sb3_contrib/crossq/policies.py
+++ b/sb3_contrib/crossq/policies.py
@@ -204,6 +204,17 @@ def action_log_prob(self, obs: PyTorchObs) -> Tuple[th.Tensor, th.Tensor]:
     def _predict(self, observation: PyTorchObs, deterministic: bool = False) -> th.Tensor:
         return self(observation, deterministic)
 
+    def set_bn_training_mode(self, mode: bool) -> None:
+        """
+        Set the training mode of the BatchRenorm layers.
+        When training is True, the running statistics are updated.
+
+        :param mode: Whether to set the layers in training mode or not
+        """
+        for module in self.modules():
+            if isinstance(module, BatchRenorm1d):
+                module.train(mode)
+
 
 class CrossQCritic(BaseModel):
     """
@@ -292,6 +303,17 @@ def forward(self, obs: th.Tensor, actions: th.Tensor) -> Tuple[th.Tensor, ...]:
         qvalue_input = th.cat([features, actions], dim=1)
         return tuple(q_net(qvalue_input) for q_net in self.q_networks)
 
+    def set_bn_training_mode(self, mode: bool) -> None:
+        """
+        Set the training mode of the BatchRenorm layers.
+        When training is True, the running statistics are updated.
+
+        :param mode: Whether to set the layers in training mode or not
+        """
+        for module in self.modules():
+            if isinstance(module, BatchRenorm1d):
+                module.train(mode)
+
 
 class CrossQPolicy(BasePolicy):
     """

From cb1bc8faf02e6cfd7b570d072976e8a0abfa1a68 Mon Sep 17 00:00:00 2001
From: Antonin RAFFIN <antonin.raffin@ensta.org>
Date: Sat, 20 Jul 2024 16:09:27 +0200
Subject: [PATCH 22/32] Simplify network creation with new SB3 version, and fix
 default momentum

---
 docs/misc/changelog.rst        |  2 +-
 sb3_contrib/crossq/policies.py | 73 ++++++++++++++++++----------------
 sb3_contrib/version.txt        |  2 +-
 setup.py                       |  2 +-
 4 files changed, 42 insertions(+), 37 deletions(-)

diff --git a/docs/misc/changelog.rst b/docs/misc/changelog.rst
index e8b55237..cec72857 100644
--- a/docs/misc/changelog.rst
+++ b/docs/misc/changelog.rst
@@ -3,7 +3,7 @@
 Changelog
 ==========
 
-Release 2.4.0a5 (WIP)
+Release 2.4.0a6 (WIP)
 --------------------------
 
 Breaking Changes:
diff --git a/sb3_contrib/crossq/policies.py b/sb3_contrib/crossq/policies.py
index 7a585a58..04858fc1 100644
--- a/sb3_contrib/crossq/policies.py
+++ b/sb3_contrib/crossq/policies.py
@@ -1,3 +1,4 @@
+from functools import partial
 from typing import Any, Dict, List, Optional, Tuple, Type, Union
 
 import torch as th
@@ -91,24 +92,28 @@ def __init__(
         self.clip_mean = clip_mean
 
         action_dim = get_action_dim(self.action_space)
-        latent_pi_net = create_mlp(features_dim, -1, net_arch, activation_fn)
 
-        batch_norm_params = {
-            "momentum": batch_norm_momentum,
-            "eps": batch_norm_eps,
-            "warmup_steps": renorm_warmup_steps,
-        }
-
-        # TODO(antonin): refactor once we can have batch norm/custom layers easily in SB3
+        pre_linear_modules = []
         if batch_norm:
-            # If batchnorm, then we want to add torch.nn.Batch_Norm layers before every linear layer
-            net: List[Union[nn.Module, BatchRenorm1d]] = []
-            for layer in latent_pi_net:
-                if isinstance(layer, nn.Linear):
-                    net.append(BatchRenorm1d(layer.in_features, **batch_norm_params))  # type: ignore[arg-type]
-                net.append(layer)
-            net.append(BatchRenorm1d(net_arch[-1], **batch_norm_params))  # type: ignore[arg-type]
-            latent_pi_net = net
+            pre_linear_modules = [
+                partial(
+                    BatchRenorm1d,
+                    momentum=batch_norm_momentum,
+                    eps=batch_norm_eps,
+                    warmup_steps=renorm_warmup_steps,
+                )
+            ]
+
+        latent_pi_net = create_mlp(
+            features_dim,
+            -1,
+            net_arch,
+            activation_fn,
+            pre_linear_modules=pre_linear_modules,  # type: ignore[arg-type]
+        )
+
+        if batch_norm and net_arch:
+            latent_pi_net.append(pre_linear_modules[0](net_arch[-1]))
 
         self.latent_pi = nn.Sequential(*latent_pi_net)
         last_layer_dim = net_arch[-1] if len(net_arch) > 0 else features_dim
@@ -269,28 +274,28 @@ def __init__(
         )
 
         action_dim = get_action_dim(self.action_space)
-        batch_norm_params = {
-            "momentum": batch_norm_momentum,
-            "eps": batch_norm_eps,
-            "warmup_steps": renorm_warmup_steps,
-        }
+        pre_linear_modules = []
+        if batch_norm:
+            pre_linear_modules = [
+                partial(
+                    BatchRenorm1d,
+                    momentum=batch_norm_momentum,
+                    eps=batch_norm_eps,
+                    warmup_steps=renorm_warmup_steps,
+                )
+            ]
 
         self.share_features_extractor = share_features_extractor
         self.n_critics = n_critics
         self.q_networks: List[nn.Module] = []
         for idx in range(n_critics):
-            q_net_list = create_mlp(features_dim + action_dim, 1, net_arch, activation_fn)
-
-            # TODO(antonin): refactor once we can have batch norm/custom layers easily in SB3
-            if batch_norm:
-                # If batchnorm, then we want to add torch.nn.Batch_Norm layers before every linear layer
-                net: List[Union[nn.Module, BatchRenorm1d]] = []
-                for layer in q_net_list:
-                    if isinstance(layer, nn.Linear):
-                        net.append(BatchRenorm1d(layer.in_features, **batch_norm_params))  # type: ignore[arg-type]
-                    net.append(layer)
-                q_net_list = net
-
+            q_net_list = create_mlp(
+                features_dim + action_dim,
+                1,
+                net_arch,
+                activation_fn,
+                pre_linear_modules=pre_linear_modules,  # type: ignore[arg-type]
+            )
             q_net = nn.Sequential(*q_net_list)
             self.add_module(f"qf{idx}", q_net)
             self.q_networks.append(q_net)
@@ -360,7 +365,7 @@ def __init__(
         net_arch: Optional[Union[List[int], Dict[str, List[int]]]] = None,
         activation_fn: Type[nn.Module] = nn.ReLU,
         batch_norm: bool = True,
-        batch_norm_momentum: float = 0.99,
+        batch_norm_momentum: float = 0.01,  # Note: Jax implementation is 1 - momentum = 0.99
         batch_norm_eps: float = 0.001,
         renorm_warmup_steps: int = 100_000,
         use_sde: bool = False,
diff --git a/sb3_contrib/version.txt b/sb3_contrib/version.txt
index a1fd35b5..464a5c4d 100644
--- a/sb3_contrib/version.txt
+++ b/sb3_contrib/version.txt
@@ -1 +1 @@
-2.4.0a5
+2.4.0a6
diff --git a/setup.py b/setup.py
index cdb9d67a..c8e00ec7 100644
--- a/setup.py
+++ b/setup.py
@@ -67,7 +67,7 @@
     packages=[package for package in find_packages() if package.startswith("sb3_contrib")],
     package_data={"sb3_contrib": ["py.typed", "version.txt"]},
     install_requires=[
-        "stable_baselines3>=2.4.0a4,<3.0",
+        "stable_baselines3>=2.4.0a6,<3.0",
     ],
     description="Contrib package of Stable Baselines3, experimental code.",
     author="Antonin Raffin",

From a88a19b234db1dcf276192d2088bda1b26afb3ee Mon Sep 17 00:00:00 2001
From: Antonin RAFFIN <antonin.raffin@ensta.org>
Date: Sat, 20 Jul 2024 16:23:46 +0200
Subject: [PATCH 23/32] Use different b1 for Adam as in original implementation

---
 sb3_contrib/crossq/policies.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/sb3_contrib/crossq/policies.py b/sb3_contrib/crossq/policies.py
index 04858fc1..3b5f0de2 100644
--- a/sb3_contrib/crossq/policies.py
+++ b/sb3_contrib/crossq/policies.py
@@ -365,6 +365,7 @@ def __init__(
         net_arch: Optional[Union[List[int], Dict[str, List[int]]]] = None,
         activation_fn: Type[nn.Module] = nn.ReLU,
         batch_norm: bool = True,
+        # TODO: double check the default value of batch_norm_momentum
         batch_norm_momentum: float = 0.01,  # Note: Jax implementation is 1 - momentum = 0.99
         batch_norm_eps: float = 0.001,
         renorm_warmup_steps: int = 100_000,
@@ -380,6 +381,14 @@ def __init__(
         n_critics: int = 2,
         share_features_extractor: bool = False,
     ):
+        if optimizer_kwargs is None:
+            # Note: the default value for b1 is 0.9 in Adam.
+            # b1=0.5 is used in the original CrossQ implementation
+            # but shows only little overall improvement.
+            optimizer_kwargs = {}
+            if optimizer_class in [th.optim.Adam, th.optim.AdamW]:
+                optimizer_kwargs["betas"] = (0.5, 0.999)
+
         super().__init__(
             observation_space,
             action_space,

From 32f66fe0388c1800156bd7e2549ac469810ef8ac Mon Sep 17 00:00:00 2001
From: Antonin RAFFIN <antonin.raffin@ensta.org>
Date: Sat, 20 Jul 2024 16:35:43 +0200
Subject: [PATCH 24/32] Reformat TOML file

---
 pyproject.toml | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 7aaad514..72e01933 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,7 +12,7 @@ ignore = ["B028", "RUF013"]
 
 [tool.ruff.lint.per-file-ignores]
 # ClassVar, implicit optional check not needed for tests
-"./tests/*.py"= ["RUF012", "RUF013"]
+"./tests/*.py" = ["RUF012", "RUF013"]
 
 [tool.ruff.lint.mccabe]
 # Unlike Flake8, ruff default to a complexity level of 10.
@@ -35,17 +35,13 @@ exclude = """(?x)(
 
 [tool.pytest.ini_options]
 # Deterministic ordering for tests; useful for pytest-xdist.
-env = [
-	"PYTHONHASHSEED=0"
-]
+env = ["PYTHONHASHSEED=0"]
 
 filterwarnings = [
     # Tensorboard warnings
     "ignore::DeprecationWarning:tensorboard",
 ]
-markers = [
-    "slow: marks tests as slow (deselect with '-m \"not slow\"')"
-]
+markers = ["slow: marks tests as slow (deselect with '-m \"not slow\"')"]
 
 [tool.coverage.run]
 disable_warnings = ["couldnt-parse"]
@@ -53,4 +49,11 @@ branch = false
 omit = ["tests/*", "setup.py"]
 
 [tool.coverage.report]
-exclude_lines = [ "pragma: no cover", "raise NotImplementedError()", "if typing.TYPE_CHECKING:"]
+exclude_lines = [
+    "pragma: no cover",
+    "raise NotImplementedError()",
+    "if typing.TYPE_CHECKING:",
+]
+
+# [tool.pyright]
+# extraPaths = ["../torchy-baselines/"]

From 03db09ef40acaeb08a7a453023e872c2b8f4906e Mon Sep 17 00:00:00 2001
From: Antonin RAFFIN <antonin.raffin@ensta.org>
Date: Mon, 22 Jul 2024 13:56:00 +0200
Subject: [PATCH 25/32] Update CI workflow, skip mypy for 3.8

---
 .github/workflows/ci.yml | 78 ++++++++++++++++++++--------------------
 1 file changed, 40 insertions(+), 38 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 6f81953f..dd8d92cf 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -5,9 +5,9 @@ name: CI
 
 on:
   push:
-    branches: [ master ]
+    branches: [master]
   pull_request:
-    branches: [ master ]
+    branches: [master]
 
 jobs:
   build:
@@ -22,42 +22,44 @@ jobs:
         python-version: ["3.8", "3.9", "3.10", "3.11"]
 
     steps:
-    - uses: actions/checkout@v3
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
-      with:
-        python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        # cpu version of pytorch
-        pip install torch==2.1.1 --index-url https://download.pytorch.org/whl/cpu
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          # cpu version of pytorch
+          pip install torch==2.1.1 --index-url https://download.pytorch.org/whl/cpu
 
-        # Install Atari Roms
-        pip install autorom
-        wget https://gist.githubusercontent.com/jjshoots/61b22aefce4456920ba99f2c36906eda/raw/00046ac3403768bfe45857610a3d333b8e35e026/Roms.tar.gz.b64
-        base64 Roms.tar.gz.b64 --decode &> Roms.tar.gz
-        AutoROM --accept-license --source-file Roms.tar.gz
+          # Install Atari Roms
+          pip install autorom
+          wget https://gist.githubusercontent.com/jjshoots/61b22aefce4456920ba99f2c36906eda/raw/00046ac3403768bfe45857610a3d333b8e35e026/Roms.tar.gz.b64
+          base64 Roms.tar.gz.b64 --decode &> Roms.tar.gz
+          AutoROM --accept-license --source-file Roms.tar.gz
 
-        # Install master version
-        # and dependencies for docs and tests
-        pip install "stable_baselines3[extra_no_roms,tests,docs] @ git+https://github.com/DLR-RM/stable-baselines3"
-        pip install .
-        # Use headless version
-        pip install opencv-python-headless
+          # Install master version
+          # and dependencies for docs and tests
+          pip install "stable_baselines3[extra_no_roms,tests,docs] @ git+https://github.com/DLR-RM/stable-baselines3"
+          pip install .
+          # Use headless version
+          pip install opencv-python-headless
 
-    - name: Lint with ruff
-      run: |
-        make lint
-    - name: Check codestyle
-      run: |
-        make check-codestyle
-    - name: Build the doc
-      run: |
-        make doc
-    - name: Type check
-      run: |
-        make type
-    - name: Test with pytest
-      run: |
-        make pytest
+      - name: Lint with ruff
+        run: |
+          make lint
+      - name: Check codestyle
+        run: |
+          make check-codestyle
+      - name: Build the doc
+        run: |
+          make doc
+      - name: Type check
+        run: |
+          make type
+        # Do not run for python 3.8 (mypy internal error)
+        if: matrix.python-version != '3.8'
+      - name: Test with pytest
+        run: |
+          make pytest

From 72abe85a2149e8617b4ab4035096735b393e1bca Mon Sep 17 00:00:00 2001
From: Antonin RAFFIN <antonin.raffin@ensta.org>
Date: Thu, 24 Oct 2024 08:45:54 +0200
Subject: [PATCH 26/32] Update CrossQ doc

---
 docs/guide/examples.rst        | 12 +++++++++++-
 docs/misc/changelog.rst        |  4 +++-
 docs/modules/crossq.rst        |  8 +++++---
 docs/modules/ppo_recurrent.rst |  1 -
 sb3_contrib/crossq/crossq.py   |  1 +
 sb3_contrib/crossq/policies.py |  5 ++---
 sb3_contrib/version.txt        |  2 +-
 7 files changed, 23 insertions(+), 10 deletions(-)

diff --git a/docs/guide/examples.rst b/docs/guide/examples.rst
index ebe80446..5e152265 100644
--- a/docs/guide/examples.rst
+++ b/docs/guide/examples.rst
@@ -123,6 +123,16 @@ Train a CrossQ agent on the Pendulum environment.
 
  from sb3_contrib import CrossQ
 
- model = CrossQ("MlpPolicy", "Pendulum-v1", verbose=1, policy_kwargs=dict(net_arch=dict(pi=[256, 256], qf=[1024, 1024])))
+ model = CrossQ(
+     "MlpPolicy",
+     "Pendulum-v1",
+     verbose=1,
+     policy_kwargs=dict(
+         net_arch=dict(
+             pi=[256, 256],
+             qf=[1024, 1024],
+         )
+     ),
+ )
  model.learn(total_timesteps=5_000, log_interval=4)
  model.save("crossq_pendulum")
diff --git a/docs/misc/changelog.rst b/docs/misc/changelog.rst
index eac5b51a..a023ccbf 100644
--- a/docs/misc/changelog.rst
+++ b/docs/misc/changelog.rst
@@ -3,9 +3,11 @@
 Changelog
 ==========
 
-Release 2.4.0a9 (WIP)
+Release 2.4.0a10 (WIP)
 --------------------------
 
+**New algorithm: added CrossQ**
+
 Breaking Changes:
 ^^^^^^^^^^^^^^^^^
 - Upgraded to Stable-Baselines3 >= 2.4.0
diff --git a/docs/modules/crossq.rst b/docs/modules/crossq.rst
index 719f5ec4..de3c0de5 100644
--- a/docs/modules/crossq.rst
+++ b/docs/modules/crossq.rst
@@ -10,9 +10,9 @@ Implementation of CrossQ proposed in:
 
 `Bhatt A.* & Palenicek D.* et al. Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity. ICLR 2024.`
 
-CrossQ is a simple and efficient algorithm that uses batch normalization to improve the sample efficiency of off-policy deep reinforcement learning algorithms.
+CrossQ is an algorithm that uses batch normalization to improve the sample efficiency of off-policy deep reinforcement learning algorithms.
 It is based on the idea of carefully introducing batch normalization layers in the critic network and dropping target networks.
-This yield a simpler and more sample-efficient algorithm without requiring high update-to-data ratios.
+This results in a simpler and more sample-efficient algorithm without requiring high update-to-data ratios.
 
 .. rubric:: Available Policies
 
@@ -25,7 +25,6 @@ This yield a simpler and more sample-efficient algorithm without requiring high
 
   Compared to the original implementation, the default network architecture for the q-value function is ``[1024, 1024]``
   instead of ``[2048, 2048]`` as it provides a good compromise between speed and performance.
-  We also use the default hyperparameters for Adam, since they have little impact on performance.
 
 .. note::
 
@@ -80,6 +79,9 @@ Compared to results from the original paper as well as a version from `SBX <http
 .. image:: ../images/crossQ_performance.png
 
 
+Open RL benchmark report: https://wandb.ai/openrlbenchmark/sb3-contrib/reports/SB3-Contrib-CrossQ--Vmlldzo4NTE2MTEx
+
+
 How to replicate the results?
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
diff --git a/docs/modules/ppo_recurrent.rst b/docs/modules/ppo_recurrent.rst
index e62c2e24..52d02d0d 100644
--- a/docs/modules/ppo_recurrent.rst
+++ b/docs/modules/ppo_recurrent.rst
@@ -109,7 +109,6 @@ Clone the repo for the experiment:
 
    git clone https://github.com/DLR-RM/rl-baselines3-zoo
    cd rl-baselines3-zoo
-   git checkout feat/recurrent-ppo
 
 
 Run the benchmark (replace ``$ENV_ID`` by the envs mentioned above):
diff --git a/sb3_contrib/crossq/crossq.py b/sb3_contrib/crossq/crossq.py
index 2a744cfd..572d4fbb 100644
--- a/sb3_contrib/crossq/crossq.py
+++ b/sb3_contrib/crossq/crossq.py
@@ -19,6 +19,7 @@ class CrossQ(OffPolicyAlgorithm):
     """
     Implementation of Batch Normalization in Deep Reinforcement Learning (CrossQ).
     Paper: https://openreview.net/forum?id=PczQtTsTIX
+    Reference implementation: https://github.com/araffin/sbx
 
     :param policy: The policy model to use (MlpPolicy)
     :param env: The environment to learn from (if registered in Gym, can be str)
diff --git a/sb3_contrib/crossq/policies.py b/sb3_contrib/crossq/policies.py
index 3b5f0de2..8fedeacf 100644
--- a/sb3_contrib/crossq/policies.py
+++ b/sb3_contrib/crossq/policies.py
@@ -160,7 +160,7 @@ def get_std(self) -> th.Tensor:
         but is slightly different when using ``expln`` function
         (cf StateDependentNoiseDistribution doc).
 
-        :return:
+        :return: Standard deviation of the action dist when available.
         """
         msg = "get_std() is only available when using gSDE"
         assert isinstance(self.action_dist, StateDependentNoiseDistribution), msg
@@ -224,7 +224,7 @@ def set_bn_training_mode(self, mode: bool) -> None:
 class CrossQCritic(BaseModel):
     """
     Critic network(s) for CrossQ.
-    The differnce with standard critic networks used by SAC/TD3 is that it uses BatchRenorm layers.
+    The difference with standard critic networks used by SAC/TD3 is that it uses BatchRenorm layers.
 
     By default, it creates two critic networks used to reduce overestimation
     thanks to clipped Q-learning (cf TD3 paper).
@@ -365,7 +365,6 @@ def __init__(
         net_arch: Optional[Union[List[int], Dict[str, List[int]]]] = None,
         activation_fn: Type[nn.Module] = nn.ReLU,
         batch_norm: bool = True,
-        # TODO: double check the default value of batch_norm_momentum
         batch_norm_momentum: float = 0.01,  # Note: Jax implementation is 1 - momentum = 0.99
         batch_norm_eps: float = 0.001,
         renorm_warmup_steps: int = 100_000,
diff --git a/sb3_contrib/version.txt b/sb3_contrib/version.txt
index 7c252f11..852a32b3 100644
--- a/sb3_contrib/version.txt
+++ b/sb3_contrib/version.txt
@@ -1 +1 @@
-2.4.0a9
\ No newline at end of file
+2.4.0a10

From f1fc8f5a8738ff6ffabc20429010175c6c791b1b Mon Sep 17 00:00:00 2001
From: Antonin RAFFIN <antonin.raffin@ensta.org>
Date: Thu, 24 Oct 2024 08:47:52 +0200
Subject: [PATCH 27/32] Use uv to download packages on github CI

---
 .github/workflows/ci.yml | 12 +++++++-----
 docs/misc/changelog.rst  |  1 +
 2 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index dd8d92cf..72304a1e 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -30,21 +30,23 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
+          # Use uv for faster downloads
+          pip install uv
           # cpu version of pytorch
-          pip install torch==2.1.1 --index-url https://download.pytorch.org/whl/cpu
+          uv pip install torch==2.1.1 --index-url https://download.pytorch.org/whl/cpu
 
           # Install Atari Roms
-          pip install autorom
+          uv pip install autorom
           wget https://gist.githubusercontent.com/jjshoots/61b22aefce4456920ba99f2c36906eda/raw/00046ac3403768bfe45857610a3d333b8e35e026/Roms.tar.gz.b64
           base64 Roms.tar.gz.b64 --decode &> Roms.tar.gz
           AutoROM --accept-license --source-file Roms.tar.gz
 
           # Install master version
           # and dependencies for docs and tests
-          pip install "stable_baselines3[extra_no_roms,tests,docs] @ git+https://github.com/DLR-RM/stable-baselines3"
-          pip install .
+          uv pip install "stable_baselines3[extra_no_roms,tests,docs] @ git+https://github.com/DLR-RM/stable-baselines3"
+          uv pip install .
           # Use headless version
-          pip install opencv-python-headless
+          uv pip install opencv-python-headless
 
       - name: Lint with ruff
         run: |
diff --git a/docs/misc/changelog.rst b/docs/misc/changelog.rst
index a023ccbf..d86094c7 100644
--- a/docs/misc/changelog.rst
+++ b/docs/misc/changelog.rst
@@ -31,6 +31,7 @@ Others:
 ^^^^^^^
 - Updated PyTorch version on CI to 2.3.1
 - Remove unnecessary SDE noise resampling in PPO/TRPO update
+- Switched to uv to download packages on GitHub CI
 
 Documentation:
 ^^^^^^^^^^^^^^

From 497f5fe9086ae37737416d14cbe87d8b637f56e3 Mon Sep 17 00:00:00 2001
From: Antonin RAFFIN <antonin.raffin@ensta.org>
Date: Thu, 24 Oct 2024 08:53:57 +0200
Subject: [PATCH 28/32] System install for Github CI

---
 .github/workflows/ci.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 72304a1e..41d9a5c2 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -33,20 +33,20 @@ jobs:
           # Use uv for faster downloads
           pip install uv
           # cpu version of pytorch
-          uv pip install torch==2.1.1 --index-url https://download.pytorch.org/whl/cpu
+          uv pip install --system torch==2.1.1 --index-url https://download.pytorch.org/whl/cpu
 
           # Install Atari Roms
-          uv pip install autorom
+          uv pip install --system autorom
           wget https://gist.githubusercontent.com/jjshoots/61b22aefce4456920ba99f2c36906eda/raw/00046ac3403768bfe45857610a3d333b8e35e026/Roms.tar.gz.b64
           base64 Roms.tar.gz.b64 --decode &> Roms.tar.gz
           AutoROM --accept-license --source-file Roms.tar.gz
 
           # Install master version
           # and dependencies for docs and tests
-          uv pip install "stable_baselines3[extra_no_roms,tests,docs] @ git+https://github.com/DLR-RM/stable-baselines3"
-          uv pip install .
+          uv pip install --system "stable_baselines3[extra_no_roms,tests,docs] @ git+https://github.com/DLR-RM/stable-baselines3"
+          uv pip install --system .
           # Use headless version
-          uv pip install opencv-python-headless
+          uv pip install --system opencv-python-headless
 
       - name: Lint with ruff
         run: |

From 6e378053c8e4883c73cd8924ddc157819fbb5219 Mon Sep 17 00:00:00 2001
From: Antonin RAFFIN <antonin.raffin@ensta.org>
Date: Thu, 24 Oct 2024 09:10:13 +0200
Subject: [PATCH 29/32] Fix for pytorch install

---
 .github/workflows/ci.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 41d9a5c2..34d8de9f 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -33,7 +33,8 @@ jobs:
           # Use uv for faster downloads
           pip install uv
           # cpu version of pytorch
-          uv pip install --system torch==2.1.1 --index-url https://download.pytorch.org/whl/cpu
+          # See https://github.com/astral-sh/uv/issues/1497
+          uv pip install --system torch==2.5.0.* --index https://download.pytorch.org/whl/cpu
 
           # Install Atari Roms
           uv pip install --system autorom

From 94af8533170d14c216cc6b71f4bb0ad21f752a66 Mon Sep 17 00:00:00 2001
From: Antonin RAFFIN <antonin.raffin@ensta.org>
Date: Thu, 24 Oct 2024 09:12:04 +0200
Subject: [PATCH 30/32] Use +cpu version

---
 .github/workflows/ci.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 34d8de9f..84e3371c 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -34,7 +34,7 @@ jobs:
           pip install uv
           # cpu version of pytorch
           # See https://github.com/astral-sh/uv/issues/1497
-          uv pip install --system torch==2.5.0.* --index https://download.pytorch.org/whl/cpu
+          uv pip install --system torch==2.5.0+cpu --index https://download.pytorch.org/whl/cpu
 
           # Install Atari Roms
           uv pip install --system autorom

From 6cd924ecf5118ffbd15583300c6eadb1cdbe340b Mon Sep 17 00:00:00 2001
From: Antonin RAFFIN <antonin.raffin@ensta.org>
Date: Thu, 24 Oct 2024 09:13:41 +0200
Subject: [PATCH 31/32] Pytorch 2.5.0 doesn't support python 3.8

---
 .github/workflows/ci.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 84e3371c..8f924ec0 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -34,7 +34,7 @@ jobs:
           pip install uv
           # cpu version of pytorch
           # See https://github.com/astral-sh/uv/issues/1497
-          uv pip install --system torch==2.5.0+cpu --index https://download.pytorch.org/whl/cpu
+          uv pip install --system torch==2.4.1+cpu --index https://download.pytorch.org/whl/cpu
 
           # Install Atari Roms
           uv pip install --system autorom

From 125a8ca0e2839cb531a6bae787820fd1e6d66e47 Mon Sep 17 00:00:00 2001
From: Antonin RAFFIN <antonin.raffin@ensta.org>
Date: Thu, 24 Oct 2024 09:38:24 +0200
Subject: [PATCH 32/32] Update comments

---
 sb3_contrib/crossq/crossq.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/sb3_contrib/crossq/crossq.py b/sb3_contrib/crossq/crossq.py
index 572d4fbb..73204eb4 100644
--- a/sb3_contrib/crossq/crossq.py
+++ b/sb3_contrib/crossq/crossq.py
@@ -221,7 +221,7 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
                 next_actions, next_log_prob = self.actor.action_log_prob(replay_data.next_observations)
 
             # Joint forward pass of obs/next_obs and actions/next_state_actions to have only
-            # one forward pass with shape (n_critics, 2 * batch_size, 1).
+            # one forward pass.
             #
             # This has two reasons:
             # 1. According to the paper obs/actions and next_obs/next_state_actions are differently
@@ -241,6 +241,7 @@ def train(self, gradient_steps: int, batch_size: int = 64) -> None:
             self.critic.set_bn_training_mode(True)
             all_q_values = th.cat(self.critic(all_obs, all_actions), dim=1)
             self.critic.set_bn_training_mode(False)
+            # (2 * batch_size, n_critics) -> (batch_size, n_critics), (batch_size, n_critics)
             current_q_values, next_q_values = th.split(all_q_values, batch_size, dim=0)
             # (batch_size, n_critics) -> (n_critics, batch_size, 1)
             current_q_values = current_q_values.T[..., None]