Source code for tianshou.policy.base

from abc import ABC, abstractmethod
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import gymnasium as gym
import numpy as np
import torch
from gymnasium.spaces import Box, Discrete, MultiBinary, MultiDiscrete
from numba import njit
from torch import nn

from import Batch, ReplayBuffer, to_numpy, to_torch_as
from tianshou.utils import MultipleLRSchedulers

[docs]class BasePolicy(ABC, nn.Module): """The base class for any RL policy. Tianshou aims to modularize RL algorithms. It comes into several classes of policies in Tianshou. All of the policy classes must inherit :class:`~tianshou.policy.BasePolicy`. A policy class typically has the following parts: * :meth:`~tianshou.policy.BasePolicy.__init__`: initialize the policy, including \ coping the target network and so on; * :meth:`~tianshou.policy.BasePolicy.forward`: compute action with given \ observation; * :meth:`~tianshou.policy.BasePolicy.process_fn`: pre-process data from the \ replay buffer (this function can interact with replay buffer); * :meth:`~tianshou.policy.BasePolicy.learn`: update policy with a given batch of \ data. * :meth:`~tianshou.policy.BasePolicy.post_process_fn`: update the replay buffer \ from the learning process (e.g., prioritized replay buffer needs to update \ the weight); * :meth:`~tianshou.policy.BasePolicy.update`: the main interface for training, \ i.e., `process_fn -> learn -> post_process_fn`. Most of the policy needs a neural network to predict the action and an optimizer to optimize the policy. The rules of self-defined networks are: 1. Input: observation "obs" (may be a ``numpy.ndarray``, a ``torch.Tensor``, a \ dict or any others), hidden state "state" (for RNN usage), and other information \ "info" provided by the environment. 2. Output: some "logits", the next hidden state "state", and the intermediate \ result during policy forwarding procedure "policy". The "logits" could be a tuple \ instead of a ``torch.Tensor``. It depends on how the policy process the network \ output. For example, in PPO, the return of the network might be \ ``(mu, sigma), state`` for Gaussian policy. The "policy" can be a Batch of \ torch.Tensor or other things, which will be stored in the replay buffer, and can \ be accessed in the policy update process (e.g. in "policy.learn()", the \ "batch.policy" is what you need). Since :class:`~tianshou.policy.BasePolicy` inherits ``torch.nn.Module``, you can use :class:`~tianshou.policy.BasePolicy` almost the same as ``torch.nn.Module``, for instance, loading and saving the model: ::, "policy.pth") policy.load_state_dict(torch.load("policy.pth")) """ def __init__( self, observation_space: Optional[gym.Space] = None, action_space: Optional[gym.Space] = None, action_scaling: bool = False, action_bound_method: str = "", lr_scheduler: Optional[Union[torch.optim.lr_scheduler.LambdaLR, MultipleLRSchedulers]] = None, ) -> None: super().__init__() self.observation_space = observation_space self.action_space = action_space self.action_type = "" if isinstance(action_space, (Discrete, MultiDiscrete, MultiBinary)): self.action_type = "discrete" elif isinstance(action_space, Box): self.action_type = "continuous" self.agent_id = 0 self.updating = False self.action_scaling = action_scaling # can be one of ("clip", "tanh", ""), empty string means no bounding assert action_bound_method in ("", "clip", "tanh") self.action_bound_method = action_bound_method self.lr_scheduler = lr_scheduler self._compile()
[docs] def set_agent_id(self, agent_id: int) -> None: """Set self.agent_id = agent_id, for MARL.""" self.agent_id = agent_id
[docs] def exploration_noise(self, act: Union[np.ndarray, Batch], batch: Batch) -> Union[np.ndarray, Batch]: """Modify the action from policy.forward with exploration noise. :param act: a data batch or numpy.ndarray which is the action taken by policy.forward. :param batch: the input batch for policy.forward, kept for advanced usage. :return: action in the same form of input "act" but with added exploration noise. """ return act
[docs] def soft_update(self, tgt: nn.Module, src: nn.Module, tau: float) -> None: """Softly update the parameters of target module towards the parameters \ of source module.""" for tgt_param, src_param in zip(tgt.parameters(), src.parameters()): * + (1 - tau) *
[docs] @abstractmethod def forward( self, batch: Batch, state: Optional[Union[dict, Batch, np.ndarray]] = None, **kwargs: Any, ) -> Batch: """Compute action over the given batch data. :return: A :class:`` which MUST have the following keys: * ``act`` an numpy.ndarray or a torch.Tensor, the action over \ given batch data. * ``state`` a dict, an numpy.ndarray or a torch.Tensor, the \ internal state of the policy, ``None`` as default. Other keys are user-defined. It depends on the algorithm. For example, :: # some code return Batch(logits=..., act=..., state=None, dist=...) The keyword ``policy`` is reserved and the corresponding data will be stored into the replay buffer. For instance, :: # some code return Batch(..., policy=Batch(log_prob=dist.log_prob(act))) # and in the sampled data batch, you can directly use # batch.policy.log_prob to get your data. .. note:: In continuous action space, you should do another step "map_action" to get the real action: :: act = policy(batch).act # doesn't map to the target action range act = policy.map_action(act, batch) """ pass
[docs] def map_action(self, act: Union[Batch, np.ndarray]) -> Union[Batch, np.ndarray]: """Map raw network output to action range in gym's env.action_space. This function is called in :meth:`` and only affects action sending to env. Remapped action will not be stored in buffer and thus can be viewed as a part of env (a black box action transformation). Action mapping includes 2 standard procedures: bounding and scaling. Bounding procedure expects original action range is (-inf, inf) and maps it to [-1, 1], while scaling procedure expects original action range is (-1, 1) and maps it to [action_space.low, action_space.high]. Bounding procedure is applied first. :param act: a data batch or numpy.ndarray which is the action taken by policy.forward. :return: action in the same form of input "act" but remap to the target action space. """ if isinstance(self.action_space, gym.spaces.Box) and \ isinstance(act, np.ndarray): # currently this action mapping only supports np.ndarray action if self.action_bound_method == "clip": act = np.clip(act, -1.0, 1.0) elif self.action_bound_method == "tanh": act = np.tanh(act) if self.action_scaling: assert np.min(act) >= -1.0 and np.max(act) <= 1.0, \ "action scaling only accepts raw action range = [-1, 1]" low, high = self.action_space.low, self.action_space.high act = low + (high - low) * (act + 1.0) / 2.0 # type: ignore return act
[docs] def map_action_inverse( self, act: Union[Batch, List, np.ndarray] ) -> Union[Batch, List, np.ndarray]: """Inverse operation to :meth:`~tianshou.policy.BasePolicy.map_action`. This function is called in :meth:`` for random initial steps. It scales [action_space.low, action_space.high] to the value ranges of policy.forward. :param act: a data batch, list or numpy.ndarray which is the action taken by gym.spaces.Box.sample(). :return: action remapped. """ if isinstance(self.action_space, gym.spaces.Box): act = to_numpy(act) if isinstance(act, np.ndarray): if self.action_scaling: low, high = self.action_space.low, self.action_space.high scale = high - low eps = np.finfo(np.float32).eps.item() scale[scale < eps] += eps act = (act - low) * 2.0 / scale - 1.0 if self.action_bound_method == "tanh": act = (np.log(1.0 + act) - np.log(1.0 - act)) / 2.0 # type: ignore return act
[docs] def process_fn( self, batch: Batch, buffer: ReplayBuffer, indices: np.ndarray ) -> Batch: """Pre-process the data from the provided replay buffer. Used in :meth:`update`. Check out :ref:`process_fn` for more information. """ return batch
[docs] @abstractmethod def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, Any]: """Update policy with a given batch of data. :return: A dict, including the data needed to be logged (e.g., loss). .. note:: In order to distinguish the collecting state, updating state and testing state, you can check the policy state by ```` and ``self.updating``. Please refer to :ref:`policy_state` for more detailed explanation. .. warning:: If you use ``torch.distributions.Normal`` and ``torch.distributions.Categorical`` to calculate the log_prob, please be careful about the shape: Categorical distribution gives "[batch_size]" shape while Normal distribution gives "[batch_size, 1]" shape. The auto-broadcasting of numerical operation with torch tensors will amplify this error. """ pass
[docs] def post_process_fn( self, batch: Batch, buffer: ReplayBuffer, indices: np.ndarray ) -> None: """Post-process the data from the provided replay buffer. Typical usage is to update the sampling weight in prioritized experience replay. Used in :meth:`update`. """ if hasattr(buffer, "update_weight") and hasattr(batch, "weight"): buffer.update_weight(indices, batch.weight)
[docs] def update(self, sample_size: int, buffer: Optional[ReplayBuffer], **kwargs: Any) -> Dict[str, Any]: """Update the policy network and replay buffer. It includes 3 function steps: process_fn, learn, and post_process_fn. In addition, this function will change the value of ``self.updating``: it will be False before this function and will be True when executing :meth:`update`. Please refer to :ref:`policy_state` for more detailed explanation. :param int sample_size: 0 means it will extract all the data from the buffer, otherwise it will sample a batch with given sample_size. :param ReplayBuffer buffer: the corresponding replay buffer. :return: A dict, including the data needed to be logged (e.g., loss) from ``policy.learn()``. """ if buffer is None: return {} batch, indices = buffer.sample(sample_size) self.updating = True batch = self.process_fn(batch, buffer, indices) result = self.learn(batch, **kwargs) self.post_process_fn(batch, buffer, indices) if self.lr_scheduler is not None: self.lr_scheduler.step() self.updating = False return result
[docs] @staticmethod def value_mask(buffer: ReplayBuffer, indices: np.ndarray) -> np.ndarray: """Value mask determines whether the obs_next of buffer[indices] is valid. For instance, usually "obs_next" after "done" flag is considered to be invalid, and its q/advantage value can provide meaningless (even misleading) information, and should be set to 0 by hand. But if "done" flag is generated because timelimit of game length (info["TimeLimit.truncated"] is set to True in gym's settings), "obs_next" will instead be valid. Value mask is typically used for assisting in calculating the correct q/advantage value. :param ReplayBuffer buffer: the corresponding replay buffer. :param numpy.ndarray indices: indices of replay buffer whose "obs_next" will be judged. :return: A bool type numpy.ndarray in the same shape with indices. "True" means "obs_next" of that buffer[indices] is valid. """ return ~buffer.terminated[indices]
[docs] @staticmethod def compute_episodic_return( batch: Batch, buffer: ReplayBuffer, indices: np.ndarray, v_s_: Optional[Union[np.ndarray, torch.Tensor]] = None, v_s: Optional[Union[np.ndarray, torch.Tensor]] = None, gamma: float = 0.99, gae_lambda: float = 0.95, ) -> Tuple[np.ndarray, np.ndarray]: """Compute returns over given batch. Use Implementation of Generalized Advantage Estimator (arXiv:1506.02438) to calculate q/advantage value of given batch. :param Batch batch: a data batch which contains several episodes of data in sequential order. Mind that the end of each finished episode of batch should be marked by done flag, unfinished (or collecting) episodes will be recognized by buffer.unfinished_index(). :param numpy.ndarray indices: tell batch's location in buffer, batch is equal to buffer[indices]. :param np.ndarray v_s_: the value function of all next states :math:`V(s')`. :param float gamma: the discount factor, should be in [0, 1]. Default to 0.99. :param float gae_lambda: the parameter for Generalized Advantage Estimation, should be in [0, 1]. Default to 0.95. :return: two numpy arrays (returns, advantage) with each shape (bsz, ). """ rew = batch.rew if v_s_ is None: assert np.isclose(gae_lambda, 1.0) v_s_ = np.zeros_like(rew) else: v_s_ = to_numpy(v_s_.flatten()) v_s_ = v_s_ * BasePolicy.value_mask(buffer, indices) v_s = np.roll(v_s_, 1) if v_s is None else to_numpy(v_s.flatten()) end_flag = np.logical_or(batch.terminated, batch.truncated) end_flag[np.isin(indices, buffer.unfinished_index())] = True advantage = _gae_return(v_s, v_s_, rew, end_flag, gamma, gae_lambda) returns = advantage + v_s # normalization varies from each policy, so we don't do it here return returns, advantage
[docs] @staticmethod def compute_nstep_return( batch: Batch, buffer: ReplayBuffer, indice: np.ndarray, target_q_fn: Callable[[ReplayBuffer, np.ndarray], torch.Tensor], gamma: float = 0.99, n_step: int = 1, rew_norm: bool = False, ) -> Batch: r"""Compute n-step return for Q-learning targets. .. math:: G_t = \sum_{i = t}^{t + n - 1} \gamma^{i - t}(1 - d_i)r_i + \gamma^n (1 - d_{t + n}) Q_{\mathrm{target}}(s_{t + n}) where :math:`\gamma` is the discount factor, :math:`\gamma \in [0, 1]`, :math:`d_t` is the done flag of step :math:`t`. :param Batch batch: a data batch, which is equal to buffer[indice]. :param ReplayBuffer buffer: the data buffer. :param function target_q_fn: a function which compute target Q value of "obs_next" given data buffer and wanted indices. :param float gamma: the discount factor, should be in [0, 1]. Default to 0.99. :param int n_step: the number of estimation step, should be an int greater than 0. Default to 1. :param bool rew_norm: normalize the reward to Normal(0, 1), Default to False. :return: a Batch. The result will be stored in batch.returns as a torch.Tensor with the same shape as target_q_fn's return tensor. """ assert not rew_norm, \ "Reward normalization in computing n-step returns is unsupported now." rew = buffer.rew bsz = len(indice) indices = [indice] for _ in range(n_step - 1): indices.append([-1])) indices = np.stack(indices) # terminal indicates buffer indexes nstep after 'indice', # and are truncated at the end of each episode terminal = indices[-1] with torch.no_grad(): target_q_torch = target_q_fn(buffer, terminal) # (bsz, ?) target_q = to_numpy(target_q_torch.reshape(bsz, -1)) target_q = target_q * BasePolicy.value_mask(buffer, terminal).reshape(-1, 1) end_flag = buffer.done.copy() end_flag[buffer.unfinished_index()] = True target_q = _nstep_return(rew, end_flag, target_q, indices, gamma, n_step) batch.returns = to_torch_as(target_q, target_q_torch) if hasattr(batch, "weight"): # prio buffer update batch.weight = to_torch_as(batch.weight, target_q_torch) return batch
def _compile(self) -> None: f64 = np.array([0, 1], dtype=np.float64) f32 = np.array([0, 1], dtype=np.float32) b = np.array([False, True], dtype=np.bool_) i64 = np.array([[0, 1]], dtype=np.int64) _gae_return(f64, f64, f64, b, 0.1, 0.1) _gae_return(f32, f32, f64, b, 0.1, 0.1) _nstep_return(f64, b, f32.reshape(-1, 1), i64, 0.1, 1)
@njit def _gae_return( v_s: np.ndarray, v_s_: np.ndarray, rew: np.ndarray, end_flag: np.ndarray, gamma: float, gae_lambda: float, ) -> np.ndarray: returns = np.zeros(rew.shape) delta = rew + v_s_ * gamma - v_s discount = (1.0 - end_flag) * (gamma * gae_lambda) gae = 0.0 for i in range(len(rew) - 1, -1, -1): gae = delta[i] + discount[i] * gae returns[i] = gae return returns @njit def _nstep_return( rew: np.ndarray, end_flag: np.ndarray, target_q: np.ndarray, indices: np.ndarray, gamma: float, n_step: int, ) -> np.ndarray: gamma_buffer = np.ones(n_step + 1) for i in range(1, n_step + 1): gamma_buffer[i] = gamma_buffer[i - 1] * gamma target_shape = target_q.shape bsz = target_shape[0] # change target_q to 2d array target_q = target_q.reshape(bsz, -1) returns = np.zeros(target_q.shape) gammas = np.full(indices[0].shape, n_step) for n in range(n_step - 1, -1, -1): now = indices[n] gammas[end_flag[now] > 0] = n + 1 returns[end_flag[now] > 0] = 0.0 returns = rew[now].reshape(bsz, 1) + gamma * returns target_q = target_q * gamma_buffer[gammas].reshape(bsz, 1) + returns return target_q.reshape(target_shape)