Implemented CrossQ

.github/workflows/ci.yml

@@ -30,21 +30,24 @@ 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
+          # See https://github.com/astral-sh/uv/issues/1497
+          uv pip install --system torch==2.4.1+cpu --index https://download.pytorch.org/whl/cpu
 
           # Install Atari Roms
-          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
-          pip install "stable_baselines3[extra_no_roms,tests,docs] @ git+https://github.com/DLR-RM/stable-baselines3"
-          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
-          pip install opencv-python-headless
+          uv pip install --system opencv-python-headless
 
       - name: Lint with ruff
         run: |
@@ -58,6 +61,8 @@ jobs:
       - 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

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)

docs/common/torch_layers.rst

@@ -0,0 +1,7 @@
+.. _th_layers:
+
+Torch Layers
+============
+
+.. automodule:: sb3_contrib.common.torch_layers
+  :members:

docs/guide/algos.rst

@@ -10,6 +10,7 @@ Name         ``Box``     ``Discrete`` ``MultiDiscrete`` ``MultiBinary`` Multi Pr
 ============ =========== ============ ================= =============== ================
 ARS          ✔️          ❌️            ❌                ❌                ✔️
 MaskablePPO  ❌           ✔️             ✔️                ✔️               ✔️
+CrossQ       ✔️          ❌            ❌                ❌                ✔️
 QR-DQN       ️❌          ️✔️            ❌                ❌                ✔️
 RecurrentPPO ✔️           ✔️             ✔️                ✔️               ✔️
 TQC          ✔️          ❌            ❌                ❌                ✔️

docs/guide/examples.rst

@@ -113,3 +113,26 @@ 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")

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
@@ -42,6 +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
 

docs/misc/changelog.rst

@@ -3,16 +3,19 @@
 Changelog
 ==========
 
-
-Release 2.4.0a9 (WIP)
+Release 2.4.0a10 (WIP)
 --------------------------
 
+**New algorithm: added CrossQ**
+
 Breaking Changes:
 ^^^^^^^^^^^^^^^^^
 - Upgraded to Stable-Baselines3 >= 2.4.0
 
 New Features:
 ^^^^^^^^^^^^^
+- Added ``CrossQ`` algorithm, from "Batch Normalization in Deep Reinforcement Learning" paper (@danielpalen)
+- Added ``BatchRenorm`` PyTorch layer used in ``CrossQ`` (@danielpalen)
 
 Bug Fixes:
 ^^^^^^^^^^
@@ -28,6 +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:
 ^^^^^^^^^^^^^^
@@ -584,4 +588,4 @@ Contributors:
 -------------
 
 @ku2482 @guyk1971 @minhlong94 @ayeright @kronion @glmcdona @cyprienc @sgillen @Gregwar @rnederstigt @qgallouedec
-@mlodel @CppMaster @burakdmb @honglu2875 @ZikangXiong @AlexPasqua @jonasreiher @icheered @Armandpl @corentinlger
+@mlodel @CppMaster @burakdmb @honglu2875 @ZikangXiong @AlexPasqua @jonasreiher @icheered @Armandpl @danielpalen @corentinlger

docs/modules/crossq.rst

@@ -0,0 +1,134 @@
+.. _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 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 results in a simpler and more sample-efficient algorithm without requiring high update-to-data ratios.
+
+.. rubric:: Available Policies
+
+.. autosummary::
+    :nosignatures:
+
+    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.
+
+.. 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 (SB3 Jax) Implementation: https://github.com/araffin/sbx
+
+
+Can I use?
+----------
+
+-  Recurrent policies: ❌
+-  Multi processing: ✔️
+-  Gym spaces:
+
+
+============= ====== ===========
+Space         Action Observation
+============= ====== ===========
+Discrete      ❌      ✔️
+Box           ✔️      ✔️
+MultiDiscrete ❌      ✔️
+MultiBinary   ❌      ✔️
+Dict          ❌      ❌
+============= ====== ===========
+
+
+Example
+-------
+
+.. code-block:: python
+
+  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, 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
+
+
+Open RL benchmark report: https://wandb.ai/openrlbenchmark/sb3-contrib/reports/SB3-Contrib-CrossQ--Vmlldzo4NTE2MTEx
+
+
+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
+--------
+
+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:

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):

pyproject.toml

@@ -54,3 +54,6 @@ exclude_lines = [
     "raise NotImplementedError()",
     "if typing.TYPE_CHECKING:",
 ]
+
+# [tool.pyright]
+# extraPaths = ["../torchy-baselines/"]

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",