09692c84fe
Change the behavior of to_numpy and to_torch: from now on, dict is automatically converted to Batch and list is automatically converted to np.ndarray (if an error occurs, raise the exception instead of converting each element in the list).
221 lines
8.0 KiB
Python
221 lines
8.0 KiB
Python
import torch
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import numpy as np
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from typing import Any, Dict, Tuple, Union, Optional
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from tianshou.data import Batch
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from tianshou.policy import BasePolicy
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class PSRLModel(object):
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"""Implementation of Posterior Sampling Reinforcement Learning Model.
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:param np.ndarray trans_count_prior: dirichlet prior (alphas), with shape
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(n_state, n_action, n_state).
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:param np.ndarray rew_mean_prior: means of the normal priors of rewards,
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with shape (n_state, n_action).
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:param np.ndarray rew_std_prior: standard deviations of the normal priors
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of rewards, with shape (n_state, n_action).
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:param float discount_factor: in [0, 1].
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:param float epsilon: for precision control in value iteration.
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"""
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def __init__(
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self,
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trans_count_prior: np.ndarray,
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rew_mean_prior: np.ndarray,
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rew_std_prior: np.ndarray,
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discount_factor: float,
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epsilon: float,
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) -> None:
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self.trans_count = trans_count_prior
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self.n_state, self.n_action = rew_mean_prior.shape
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self.rew_mean = rew_mean_prior
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self.rew_std = rew_std_prior
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self.rew_square_sum = np.zeros_like(rew_mean_prior)
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self.rew_std_prior = rew_std_prior
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self.discount_factor = discount_factor
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self.rew_count = np.full(rew_mean_prior.shape, epsilon) # no weight
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self.eps = epsilon
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self.policy: np.ndarray
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self.value = np.zeros(self.n_state)
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self.updated = False
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self.__eps = np.finfo(np.float32).eps.item()
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def observe(
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self,
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trans_count: np.ndarray,
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rew_sum: np.ndarray,
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rew_square_sum: np.ndarray,
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rew_count: np.ndarray,
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) -> None:
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"""Add data into memory pool.
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For rewards, we have a normal prior at first. After we observed a
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reward for a given state-action pair, we use the mean value of our
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observations instead of the prior mean as the posterior mean. The
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standard deviations are in inverse proportion to the number of the
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corresponding observations.
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:param np.ndarray trans_count: the number of observations, with shape
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(n_state, n_action, n_state).
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:param np.ndarray rew_sum: total rewards, with shape
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(n_state, n_action).
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:param np.ndarray rew_square_sum: total rewards' squares, with shape
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(n_state, n_action).
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:param np.ndarray rew_count: the number of rewards, with shape
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(n_state, n_action).
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"""
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self.updated = False
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self.trans_count += trans_count
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sum_count = self.rew_count + rew_count
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self.rew_mean = (self.rew_mean * self.rew_count + rew_sum) / sum_count
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self.rew_square_sum += rew_square_sum
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raw_std2 = self.rew_square_sum / sum_count - self.rew_mean ** 2
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self.rew_std = np.sqrt(1 / (
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sum_count / (raw_std2 + self.__eps) + 1 / self.rew_std_prior ** 2))
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self.rew_count = sum_count
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def sample_trans_prob(self) -> np.ndarray:
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sample_prob = torch.distributions.Dirichlet(
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torch.from_numpy(self.trans_count)).sample().numpy()
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return sample_prob
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def sample_reward(self) -> np.ndarray:
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return np.random.normal(self.rew_mean, self.rew_std)
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def solve_policy(self) -> None:
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self.updated = True
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self.policy, self.value = self.value_iteration(
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self.sample_trans_prob(),
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self.sample_reward(),
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self.discount_factor,
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self.eps,
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self.value,
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)
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@staticmethod
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def value_iteration(
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trans_prob: np.ndarray,
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rew: np.ndarray,
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discount_factor: float,
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eps: float,
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value: np.ndarray,
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) -> Tuple[np.ndarray, np.ndarray]:
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"""Value iteration solver for MDPs.
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:param np.ndarray trans_prob: transition probabilities, with shape
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(n_state, n_action, n_state).
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:param np.ndarray rew: rewards, with shape (n_state, n_action).
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:param float eps: for precision control.
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:param float discount_factor: in [0, 1].
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:param np.ndarray value: the initialize value of value array, with
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shape (n_state, ).
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:return: the optimal policy with shape (n_state, ).
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"""
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Q = rew + discount_factor * trans_prob.dot(value)
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new_value = Q.max(axis=1)
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while not np.allclose(new_value, value, eps):
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value = new_value
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Q = rew + discount_factor * trans_prob.dot(value)
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new_value = Q.max(axis=1)
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# this is to make sure if Q(s, a1) == Q(s, a2) -> choose a1/a2 randomly
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Q += eps * np.random.randn(*Q.shape)
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return Q.argmax(axis=1), new_value
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def __call__(
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self,
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obs: np.ndarray,
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state: Any = None,
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info: Dict[str, Any] = {},
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) -> np.ndarray:
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if not self.updated:
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self.solve_policy()
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return self.policy[obs]
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class PSRLPolicy(BasePolicy):
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"""Implementation of Posterior Sampling Reinforcement Learning.
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Reference: Strens M. A Bayesian framework for reinforcement learning [C]
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//ICML. 2000, 2000: 943-950.
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:param np.ndarray trans_count_prior: dirichlet prior (alphas), with shape
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(n_state, n_action, n_state).
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:param np.ndarray rew_mean_prior: means of the normal priors of rewards,
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with shape (n_state, n_action).
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:param np.ndarray rew_std_prior: standard deviations of the normal priors
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of rewards, with shape (n_state, n_action).
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:param float discount_factor: in [0, 1].
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:param float epsilon: for precision control in value iteration.
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:param bool add_done_loop: whether to add an extra self-loop for the
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terminal state in MDP. Default to False.
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.. seealso::
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Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
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explanation.
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"""
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def __init__(
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self,
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trans_count_prior: np.ndarray,
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rew_mean_prior: np.ndarray,
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rew_std_prior: np.ndarray,
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discount_factor: float = 0.99,
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epsilon: float = 0.01,
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add_done_loop: bool = False,
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**kwargs: Any,
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) -> None:
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super().__init__(**kwargs)
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assert (
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0.0 <= discount_factor <= 1.0
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), "discount factor should be in [0, 1]"
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self.model = PSRLModel(
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trans_count_prior, rew_mean_prior, rew_std_prior,
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discount_factor, epsilon)
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self._add_done_loop = add_done_loop
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def forward(
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self,
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batch: Batch,
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state: Optional[Union[dict, Batch, np.ndarray]] = None,
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**kwargs: Any,
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) -> Batch:
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"""Compute action over the given batch data with PSRL model.
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:return: A :class:`~tianshou.data.Batch` with "act" key containing
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the action.
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.. seealso::
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Please refer to :meth:`~tianshou.policy.BasePolicy.forward` for
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more detailed explanation.
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"""
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act = self.model(batch.obs, state=state, info=batch.info)
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return Batch(act=act)
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def learn(
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self, batch: Batch, *args: Any, **kwargs: Any
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) -> Dict[str, float]:
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n_s, n_a = self.model.n_state, self.model.n_action
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trans_count = np.zeros((n_s, n_a, n_s))
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rew_sum = np.zeros((n_s, n_a))
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rew_square_sum = np.zeros((n_s, n_a))
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rew_count = np.zeros((n_s, n_a))
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for b in batch.split(size=1):
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obs, act, obs_next = b.obs, b.act, b.obs_next
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trans_count[obs, act, obs_next] += 1
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rew_sum[obs, act] += b.rew
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rew_square_sum[obs, act] += b.rew ** 2
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rew_count[obs, act] += 1
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if self._add_done_loop and b.done:
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# special operation for terminal states: add a self-loop
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trans_count[obs_next, :, obs_next] += 1
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rew_count[obs_next, :] += 1
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self.model.observe(trans_count, rew_sum, rew_square_sum, rew_count)
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return {
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"psrl/rew_mean": float(self.model.rew_mean.mean()),
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"psrl/rew_std": float(self.model.rew_std.mean()),
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}
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