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Add PSRL policy in tianshou/policy/modelbase/psrl.py. Co-authored-by: n+e <[email protected]>
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# PSRL | ||
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`NChain-v0`: `python3 psrl.py --task NChain-v0 --step-per-epoch 10 --rew-mean-prior 0 --rew-std-prior 1` | ||
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`FrozenLake-v0`: `python3 psrl.py --task FrozenLake-v0 --step-per-epoch 1000 --rew-mean-prior 0 --rew-std-prior 1 --add-done-loop --epoch 20` | ||
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`Taxi-v3`: `python3 psrl.py --task Taxi-v3 --step-per-epoch 1000 --rew-mean-prior 0 --rew-std-prior 2 --epoch 20` |
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../../test/modelbase/test_psrl.py |
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import gym | ||
import torch | ||
import pprint | ||
import argparse | ||
import numpy as np | ||
from torch.utils.tensorboard import SummaryWriter | ||
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from tianshou.policy import PSRLPolicy | ||
from tianshou.trainer import onpolicy_trainer | ||
from tianshou.data import Collector, ReplayBuffer | ||
from tianshou.env import DummyVectorEnv, SubprocVectorEnv | ||
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def get_args(): | ||
parser = argparse.ArgumentParser() | ||
parser.add_argument('--task', type=str, default='NChain-v0') | ||
parser.add_argument('--seed', type=int, default=1626) | ||
parser.add_argument('--buffer-size', type=int, default=50000) | ||
parser.add_argument('--epoch', type=int, default=5) | ||
parser.add_argument('--step-per-epoch', type=int, default=5) | ||
parser.add_argument('--collect-per-step', type=int, default=1) | ||
parser.add_argument('--training-num', type=int, default=1) | ||
parser.add_argument('--test-num', type=int, default=100) | ||
parser.add_argument('--logdir', type=str, default='log') | ||
parser.add_argument('--render', type=float, default=0.0) | ||
parser.add_argument('--rew-mean-prior', type=float, default=0.0) | ||
parser.add_argument('--rew-std-prior', type=float, default=1.0) | ||
parser.add_argument('--gamma', type=float, default=0.99) | ||
parser.add_argument('--eps', type=float, default=0.01) | ||
parser.add_argument('--add-done-loop', action='store_true') | ||
return parser.parse_known_args()[0] | ||
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def test_psrl(args=get_args()): | ||
env = gym.make(args.task) | ||
if args.task == "NChain-v0": | ||
env.spec.reward_threshold = 3647 # described in PSRL paper | ||
print("reward threshold:", env.spec.reward_threshold) | ||
args.state_shape = env.observation_space.shape or env.observation_space.n | ||
args.action_shape = env.action_space.shape or env.action_space.n | ||
# train_envs = gym.make(args.task) | ||
# train_envs = gym.make(args.task) | ||
train_envs = DummyVectorEnv( | ||
[lambda: gym.make(args.task) for _ in range(args.training_num)]) | ||
# test_envs = gym.make(args.task) | ||
test_envs = SubprocVectorEnv( | ||
[lambda: gym.make(args.task) for _ in range(args.test_num)]) | ||
# seed | ||
np.random.seed(args.seed) | ||
torch.manual_seed(args.seed) | ||
train_envs.seed(args.seed) | ||
test_envs.seed(args.seed) | ||
# model | ||
n_action = args.action_shape | ||
n_state = args.state_shape | ||
trans_count_prior = np.ones((n_state, n_action, n_state)) | ||
rew_mean_prior = np.full((n_state, n_action), args.rew_mean_prior) | ||
rew_std_prior = np.full((n_state, n_action), args.rew_std_prior) | ||
policy = PSRLPolicy( | ||
trans_count_prior, rew_mean_prior, rew_std_prior, args.gamma, args.eps, | ||
args.add_done_loop) | ||
# collector | ||
train_collector = Collector( | ||
policy, train_envs, ReplayBuffer(args.buffer_size)) | ||
test_collector = Collector(policy, test_envs) | ||
# log | ||
writer = SummaryWriter(args.logdir + '/' + args.task) | ||
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def stop_fn(x): | ||
if env.spec.reward_threshold: | ||
return x >= env.spec.reward_threshold | ||
else: | ||
return False | ||
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train_collector.collect(n_step=args.buffer_size, random=True) | ||
# trainer | ||
result = onpolicy_trainer( | ||
policy, train_collector, test_collector, args.epoch, | ||
args.step_per_epoch, args.collect_per_step, 1, | ||
args.test_num, 0, stop_fn=stop_fn, writer=writer, | ||
test_in_train=False) | ||
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if __name__ == '__main__': | ||
pprint.pprint(result) | ||
# Let's watch its performance! | ||
policy.eval() | ||
test_envs.seed(args.seed) | ||
test_collector.reset() | ||
result = test_collector.collect(n_episode=[1] * args.test_num, | ||
render=args.render) | ||
print(f'Final reward: {result["rew"]}, length: {result["len"]}') | ||
elif env.spec.reward_threshold: | ||
assert result["best_reward"] >= env.spec.reward_threshold | ||
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if __name__ == '__main__': | ||
test_psrl() |
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import torch | ||
import numpy as np | ||
from typing import Any, Dict, Union, Optional | ||
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from tianshou.data import Batch | ||
from tianshou.policy import BasePolicy | ||
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class PSRLModel(object): | ||
"""Implementation of Posterior Sampling Reinforcement Learning Model. | ||
:param np.ndarray trans_count_prior: dirichlet prior (alphas), with shape | ||
(n_state, n_action, n_state). | ||
:param np.ndarray rew_mean_prior: means of the normal priors of rewards, | ||
with shape (n_state, n_action). | ||
:param np.ndarray rew_std_prior: standard deviations of the normal priors | ||
of rewards, with shape (n_state, n_action). | ||
:param float discount_factor: in [0, 1]. | ||
:param float epsilon: for precision control in value iteration. | ||
""" | ||
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def __init__( | ||
self, | ||
trans_count_prior: np.ndarray, | ||
rew_mean_prior: np.ndarray, | ||
rew_std_prior: np.ndarray, | ||
discount_factor: float, | ||
epsilon: float, | ||
) -> None: | ||
self.trans_count = trans_count_prior | ||
self.n_state, self.n_action = rew_mean_prior.shape | ||
self.rew_mean = rew_mean_prior | ||
self.rew_std = rew_std_prior | ||
self.rew_square_sum = np.zeros_like(rew_mean_prior) | ||
self.rew_std_prior = rew_std_prior | ||
self.discount_factor = discount_factor | ||
self.rew_count = np.full(rew_mean_prior.shape, epsilon) # no weight | ||
self.eps = epsilon | ||
self.policy: np.ndarray | ||
self.value = np.zeros(self.n_state) | ||
self.updated = False | ||
self.__eps = np.finfo(np.float32).eps.item() | ||
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def observe( | ||
self, | ||
trans_count: np.ndarray, | ||
rew_sum: np.ndarray, | ||
rew_square_sum: np.ndarray, | ||
rew_count: np.ndarray, | ||
) -> None: | ||
"""Add data into memory pool. | ||
For rewards, we have a normal prior at first. After we observed a | ||
reward for a given state-action pair, we use the mean value of our | ||
observations instead of the prior mean as the posterior mean. The | ||
standard deviations are in inverse proportion to the number of the | ||
corresponding observations. | ||
:param np.ndarray trans_count: the number of observations, with shape | ||
(n_state, n_action, n_state). | ||
:param np.ndarray rew_sum: total rewards, with shape | ||
(n_state, n_action). | ||
:param np.ndarray rew_square_sum: total rewards' squares, with shape | ||
(n_state, n_action). | ||
:param np.ndarray rew_count: the number of rewards, with shape | ||
(n_state, n_action). | ||
""" | ||
self.updated = False | ||
self.trans_count += trans_count | ||
sum_count = self.rew_count + rew_count | ||
self.rew_mean = (self.rew_mean * self.rew_count + rew_sum) / sum_count | ||
self.rew_square_sum += rew_square_sum | ||
raw_std2 = self.rew_square_sum / sum_count - self.rew_mean ** 2 | ||
self.rew_std = np.sqrt(1 / ( | ||
sum_count / (raw_std2 + self.__eps) + 1 / self.rew_std_prior ** 2)) | ||
self.rew_count = sum_count | ||
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def sample_trans_prob(self) -> np.ndarray: | ||
sample_prob = torch.distributions.Dirichlet( | ||
torch.from_numpy(self.trans_count)).sample().numpy() | ||
return sample_prob | ||
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def sample_reward(self) -> np.ndarray: | ||
return np.random.normal(self.rew_mean, self.rew_std) | ||
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def solve_policy(self) -> None: | ||
self.updated = True | ||
self.policy, self.value = self.value_iteration( | ||
self.sample_trans_prob(), | ||
self.sample_reward(), | ||
self.discount_factor, | ||
self.eps, | ||
self.value, | ||
) | ||
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@staticmethod | ||
def value_iteration( | ||
trans_prob: np.ndarray, | ||
rew: np.ndarray, | ||
discount_factor: float, | ||
eps: float, | ||
value: np.ndarray, | ||
) -> np.ndarray: | ||
"""Value iteration solver for MDPs. | ||
:param np.ndarray trans_prob: transition probabilities, with shape | ||
(n_state, n_action, n_state). | ||
:param np.ndarray rew: rewards, with shape (n_state, n_action). | ||
:param float eps: for precision control. | ||
:param float discount_factor: in [0, 1]. | ||
:param np.ndarray value: the initialize value of value array, with | ||
shape (n_state, ). | ||
:return: the optimal policy with shape (n_state, ). | ||
""" | ||
Q = rew + discount_factor * trans_prob.dot(value) | ||
new_value = Q.max(axis=1) | ||
while not np.allclose(new_value, value, eps): | ||
value = new_value | ||
Q = rew + discount_factor * trans_prob.dot(value) | ||
new_value = Q.max(axis=1) | ||
# this is to make sure if Q(s, a1) == Q(s, a2) -> choose a1/a2 randomly | ||
Q += eps * np.random.randn(*Q.shape) | ||
return Q.argmax(axis=1), new_value | ||
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def __call__( | ||
self, | ||
obs: np.ndarray, | ||
state: Optional[Any] = None, | ||
info: Dict[str, Any] = {}, | ||
) -> np.ndarray: | ||
if not self.updated: | ||
self.solve_policy() | ||
return self.policy[obs] | ||
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class PSRLPolicy(BasePolicy): | ||
"""Implementation of Posterior Sampling Reinforcement Learning. | ||
Reference: Strens M. A Bayesian framework for reinforcement learning [C] | ||
//ICML. 2000, 2000: 943-950. | ||
:param np.ndarray trans_count_prior: dirichlet prior (alphas), with shape | ||
(n_state, n_action, n_state). | ||
:param np.ndarray rew_mean_prior: means of the normal priors of rewards, | ||
with shape (n_state, n_action). | ||
:param np.ndarray rew_std_prior: standard deviations of the normal priors | ||
of rewards, with shape (n_state, n_action). | ||
:param float discount_factor: in [0, 1]. | ||
:param float epsilon: for precision control in value iteration. | ||
:param bool add_done_loop: whether to add an extra self-loop for the | ||
terminal state in MDP, defaults to False. | ||
.. seealso:: | ||
Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed | ||
explanation. | ||
""" | ||
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def __init__( | ||
self, | ||
trans_count_prior: np.ndarray, | ||
rew_mean_prior: np.ndarray, | ||
rew_std_prior: np.ndarray, | ||
discount_factor: float = 0.99, | ||
epsilon: float = 0.01, | ||
add_done_loop: bool = False, | ||
**kwargs: Any, | ||
) -> None: | ||
super().__init__(**kwargs) | ||
assert ( | ||
0.0 <= discount_factor <= 1.0 | ||
), "discount factor should be in [0, 1]" | ||
self.model = PSRLModel( | ||
trans_count_prior, rew_mean_prior, rew_std_prior, | ||
discount_factor, epsilon) | ||
self._add_done_loop = add_done_loop | ||
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def forward( | ||
self, | ||
batch: Batch, | ||
state: Optional[Union[dict, Batch, np.ndarray]] = None, | ||
**kwargs: Any, | ||
) -> Batch: | ||
"""Compute action over the given batch data with PSRL model. | ||
:return: A :class:`~tianshou.data.Batch` with "act" key containing | ||
the action. | ||
.. seealso:: | ||
Please refer to :meth:`~tianshou.policy.BasePolicy.forward` for | ||
more detailed explanation. | ||
""" | ||
act = self.model(batch.obs, state=state, info=batch.info) | ||
return Batch(act=act) | ||
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def learn( | ||
self, batch: Batch, *args: Any, **kwargs: Any | ||
) -> Dict[str, float]: | ||
n_s, n_a = self.model.n_state, self.model.n_action | ||
trans_count = np.zeros((n_s, n_a, n_s)) | ||
rew_sum = np.zeros((n_s, n_a)) | ||
rew_square_sum = np.zeros((n_s, n_a)) | ||
rew_count = np.zeros((n_s, n_a)) | ||
for b in batch.split(size=1): | ||
obs, act, obs_next = b.obs, b.act, b.obs_next | ||
trans_count[obs, act, obs_next] += 1 | ||
rew_sum[obs, act] += b.rew | ||
rew_square_sum[obs, act] += b.rew ** 2 | ||
rew_count[obs, act] += 1 | ||
if self._add_done_loop and b.done: | ||
# special operation for terminal states: add a self-loop | ||
trans_count[obs_next, :, obs_next] += 1 | ||
rew_count[obs_next, :] += 1 | ||
self.model.observe(trans_count, rew_sum, rew_square_sum, rew_count) | ||
return { | ||
"psrl/rew_mean": self.model.rew_mean.mean(), | ||
"psrl/rew_std": self.model.rew_std.mean(), | ||
} |