656 lines
26 KiB
Python
656 lines
26 KiB
Python
import time
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import warnings
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from collections.abc import Callable
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from dataclasses import dataclass
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from typing import Any, cast
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import gymnasium as gym
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import numpy as np
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import torch
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from tianshou.data import (
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Batch,
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CachedReplayBuffer,
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PrioritizedReplayBuffer,
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ReplayBuffer,
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ReplayBufferManager,
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SequenceSummaryStats,
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VectorReplayBuffer,
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to_numpy,
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)
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from tianshou.data.batch import alloc_by_keys_diff
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from tianshou.data.types import RolloutBatchProtocol
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from tianshou.env import BaseVectorEnv, DummyVectorEnv
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from tianshou.policy import BasePolicy
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@dataclass(kw_only=True)
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class CollectStatsBase:
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"""The most basic stats, often used for offline learning."""
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n_collected_episodes: int = 0
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"""The number of collected episodes."""
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n_collected_steps: int = 0
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"""The number of collected steps."""
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@dataclass(kw_only=True)
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class CollectStats(CollectStatsBase):
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"""A data structure for storing the statistics of rollouts."""
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collect_time: float = 0.0
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"""The time for collecting transitions."""
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collect_speed: float = 0.0
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"""The speed of collecting (env_step per second)."""
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returns: np.ndarray
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"""The collected episode returns."""
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returns_stat: SequenceSummaryStats | None # can be None if no episode ends during collect step
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"""Stats of the collected returns."""
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lens: np.ndarray
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"""The collected episode lengths."""
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lens_stat: SequenceSummaryStats | None # can be None if no episode ends during collect step
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"""Stats of the collected episode lengths."""
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class Collector:
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"""Collector enables the policy to interact with different types of envs with exact number of steps or episodes.
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:param policy: an instance of the :class:`~tianshou.policy.BasePolicy` class.
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:param env: a ``gym.Env`` environment or an instance of the
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:class:`~tianshou.env.BaseVectorEnv` class.
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:param buffer: an instance of the :class:`~tianshou.data.ReplayBuffer` class.
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If set to None, it will not store the data. Default to None.
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:param function preprocess_fn: a function called before the data has been added to
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the buffer, see issue #42 and :ref:`preprocess_fn`. Default to None.
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:param exploration_noise: determine whether the action needs to be modified
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with corresponding policy's exploration noise. If so, "policy.
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exploration_noise(act, batch)" will be called automatically to add the
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exploration noise into action. Default to False.
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The "preprocess_fn" is a function called before the data has been added to the
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buffer with batch format. It will receive only "obs" and "env_id" when the
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collector resets the environment, and will receive the keys "obs_next", "rew",
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"terminated", "truncated, "info", "policy" and "env_id" in a normal env step.
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Alternatively, it may also accept the keys "obs_next", "rew", "done", "info",
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"policy" and "env_id".
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It returns either a dict or a :class:`~tianshou.data.Batch` with the modified
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keys and values. Examples are in "test/base/test_collector.py".
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.. note::
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Please make sure the given environment has a time limitation if using n_episode
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collect option.
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.. note::
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In past versions of Tianshou, the replay buffer that was passed to `__init__`
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was automatically reset. This is not done in the current implementation.
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"""
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def __init__(
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self,
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policy: BasePolicy,
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env: gym.Env | BaseVectorEnv,
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buffer: ReplayBuffer | None = None,
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preprocess_fn: Callable[..., RolloutBatchProtocol] | None = None,
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exploration_noise: bool = False,
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) -> None:
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super().__init__()
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if isinstance(env, gym.Env) and not hasattr(env, "__len__"):
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self.env = DummyVectorEnv([lambda: env])
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else:
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self.env = env # type: ignore
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self.env_num = len(self.env)
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self.exploration_noise = exploration_noise
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self.buffer: ReplayBuffer
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self._assign_buffer(buffer)
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self.policy = policy
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self.preprocess_fn = preprocess_fn
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self._action_space = self.env.action_space
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self.data: RolloutBatchProtocol
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# avoid creating attribute outside __init__
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self.reset(False)
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def _assign_buffer(self, buffer: ReplayBuffer | None) -> None:
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"""Check if the buffer matches the constraint."""
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if buffer is None:
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buffer = VectorReplayBuffer(self.env_num, self.env_num)
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elif isinstance(buffer, ReplayBufferManager):
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assert buffer.buffer_num >= self.env_num
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if isinstance(buffer, CachedReplayBuffer):
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assert buffer.cached_buffer_num >= self.env_num
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else: # ReplayBuffer or PrioritizedReplayBuffer
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assert buffer.maxsize > 0
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if self.env_num > 1:
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if isinstance(buffer, ReplayBuffer):
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buffer_type = "ReplayBuffer"
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vector_type = "VectorReplayBuffer"
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if isinstance(buffer, PrioritizedReplayBuffer):
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buffer_type = "PrioritizedReplayBuffer"
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vector_type = "PrioritizedVectorReplayBuffer"
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raise TypeError(
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f"Cannot use {buffer_type}(size={buffer.maxsize}, ...) to collect "
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f"{self.env_num} envs,\n\tplease use {vector_type}(total_size="
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f"{buffer.maxsize}, buffer_num={self.env_num}, ...) instead.",
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)
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self.buffer = buffer
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def reset(
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self,
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reset_buffer: bool = True,
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gym_reset_kwargs: dict[str, Any] | None = None,
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) -> None:
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"""Reset the environment, statistics, current data and possibly replay memory.
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:param reset_buffer: if true, reset the replay buffer that is attached
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to the collector.
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:param gym_reset_kwargs: extra keyword arguments to pass into the environment's
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reset function. Defaults to None (extra keyword arguments)
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"""
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# use empty Batch for "state" so that self.data supports slicing
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# convert empty Batch to None when passing data to policy
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data = Batch(
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obs={},
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act={},
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rew={},
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terminated={},
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truncated={},
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done={},
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obs_next={},
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info={},
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policy={},
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)
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self.data = cast(RolloutBatchProtocol, data)
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self.reset_env(gym_reset_kwargs)
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if reset_buffer:
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self.reset_buffer()
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self.reset_stat()
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def reset_stat(self) -> None:
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"""Reset the statistic variables."""
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self.collect_step, self.collect_episode, self.collect_time = 0, 0, 0.0
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def reset_buffer(self, keep_statistics: bool = False) -> None:
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"""Reset the data buffer."""
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self.buffer.reset(keep_statistics=keep_statistics)
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def reset_env(self, gym_reset_kwargs: dict[str, Any] | None = None) -> None:
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"""Reset all of the environments."""
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gym_reset_kwargs = gym_reset_kwargs if gym_reset_kwargs else {}
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obs, info = self.env.reset(**gym_reset_kwargs)
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if self.preprocess_fn:
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processed_data = self.preprocess_fn(obs=obs, info=info, env_id=np.arange(self.env_num))
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obs = processed_data.get("obs", obs)
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info = processed_data.get("info", info)
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self.data.info = info # type: ignore
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self.data.obs = obs
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def _reset_state(self, id: int | list[int]) -> None:
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"""Reset the hidden state: self.data.state[id]."""
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if hasattr(self.data.policy, "hidden_state"):
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state = self.data.policy.hidden_state # it is a reference
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if isinstance(state, torch.Tensor):
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state[id].zero_()
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elif isinstance(state, np.ndarray):
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state[id] = None if state.dtype == object else 0
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elif isinstance(state, Batch):
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state.empty_(id)
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def _reset_env_with_ids(
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self,
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local_ids: list[int] | np.ndarray,
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global_ids: list[int] | np.ndarray,
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gym_reset_kwargs: dict[str, Any] | None = None,
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) -> None:
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gym_reset_kwargs = gym_reset_kwargs if gym_reset_kwargs else {}
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obs_reset, info = self.env.reset(global_ids, **gym_reset_kwargs)
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if self.preprocess_fn:
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processed_data = self.preprocess_fn(obs=obs_reset, info=info, env_id=global_ids)
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obs_reset = processed_data.get("obs", obs_reset)
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info = processed_data.get("info", info)
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self.data.info[local_ids] = info # type: ignore
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self.data.obs_next[local_ids] = obs_reset # type: ignore
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def collect(
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self,
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n_step: int | None = None,
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n_episode: int | None = None,
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random: bool = False,
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render: float | None = None,
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no_grad: bool = True,
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gym_reset_kwargs: dict[str, Any] | None = None,
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) -> CollectStats:
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"""Collect a specified number of step or episode.
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To ensure unbiased sampling result with n_episode option, this function will
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first collect ``n_episode - env_num`` episodes, then for the last ``env_num``
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episodes, they will be collected evenly from each env.
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:param n_step: how many steps you want to collect.
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:param n_episode: how many episodes you want to collect.
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:param random: whether to use random policy for collecting data. Default
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to False.
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:param render: the sleep time between rendering consecutive frames.
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Default to None (no rendering).
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:param no_grad: whether to retain gradient in policy.forward(). Default to
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True (no gradient retaining).
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:param gym_reset_kwargs: extra keyword arguments to pass into the environment's
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reset function. Defaults to None (extra keyword arguments)
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.. note::
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One and only one collection number specification is permitted, either
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``n_step`` or ``n_episode``.
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:return: A dataclass object
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"""
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assert not self.env.is_async, "Please use AsyncCollector if using async venv."
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if n_step is not None:
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assert n_episode is None, (
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f"Only one of n_step or n_episode is allowed in Collector."
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f"collect, got n_step={n_step}, n_episode={n_episode}."
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)
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assert n_step > 0
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if n_step % self.env_num != 0:
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warnings.warn(
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f"n_step={n_step} is not a multiple of #env ({self.env_num}), "
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"which may cause extra transitions collected into the buffer.",
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)
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ready_env_ids = np.arange(self.env_num)
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elif n_episode is not None:
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assert n_episode > 0
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ready_env_ids = np.arange(min(self.env_num, n_episode))
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self.data = self.data[: min(self.env_num, n_episode)]
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else:
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raise TypeError(
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"Please specify at least one (either n_step or n_episode) "
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"in AsyncCollector.collect().",
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)
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start_time = time.time()
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step_count = 0
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episode_count = 0
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episode_returns: list[float] = []
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episode_lens: list[int] = []
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episode_start_indices: list[int] = []
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while True:
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assert len(self.data) == len(ready_env_ids)
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# restore the state: if the last state is None, it won't store
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last_state = self.data.policy.pop("hidden_state", None)
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# get the next action
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if random:
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try:
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act_sample = [self._action_space[i].sample() for i in ready_env_ids]
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except TypeError: # envpool's action space is not for per-env
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act_sample = [self._action_space.sample() for _ in ready_env_ids]
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act_sample = self.policy.map_action_inverse(act_sample) # type: ignore
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self.data.update(act=act_sample)
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else:
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if no_grad:
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with torch.no_grad(): # faster than retain_grad version
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# self.data.obs will be used by agent to get result
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result = self.policy(self.data, last_state)
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else:
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result = self.policy(self.data, last_state)
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# update state / act / policy into self.data
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policy = result.get("policy", Batch())
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assert isinstance(policy, Batch)
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state = result.get("state", None)
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if state is not None:
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policy.hidden_state = state # save state into buffer
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act = to_numpy(result.act)
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if self.exploration_noise:
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act = self.policy.exploration_noise(act, self.data)
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self.data.update(policy=policy, act=act)
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# get bounded and remapped actions first (not saved into buffer)
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action_remap = self.policy.map_action(self.data.act)
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# step in env
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obs_next, rew, terminated, truncated, info = self.env.step(
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action_remap,
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ready_env_ids,
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)
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done = np.logical_or(terminated, truncated)
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self.data.update(
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obs_next=obs_next,
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rew=rew,
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terminated=terminated,
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truncated=truncated,
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done=done,
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info=info,
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)
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if self.preprocess_fn:
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self.data.update(
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self.preprocess_fn(
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obs_next=self.data.obs_next,
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rew=self.data.rew,
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done=self.data.done,
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info=self.data.info,
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policy=self.data.policy,
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env_id=ready_env_ids,
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act=self.data.act,
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),
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)
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if render:
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self.env.render()
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if render > 0 and not np.isclose(render, 0):
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time.sleep(render)
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# add data into the buffer
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ptr, ep_rew, ep_len, ep_idx = self.buffer.add(self.data, buffer_ids=ready_env_ids)
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# collect statistics
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step_count += len(ready_env_ids)
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if np.any(done):
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env_ind_local = np.where(done)[0]
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env_ind_global = ready_env_ids[env_ind_local]
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episode_count += len(env_ind_local)
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episode_lens.extend(ep_len[env_ind_local])
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episode_returns.extend(ep_rew[env_ind_local])
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episode_start_indices.extend(ep_idx[env_ind_local])
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# now we copy obs_next to obs, but since there might be
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# finished episodes, we have to reset finished envs first.
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self._reset_env_with_ids(env_ind_local, env_ind_global, gym_reset_kwargs)
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for i in env_ind_local:
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self._reset_state(i)
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# remove surplus env id from ready_env_ids
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# to avoid bias in selecting environments
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if n_episode:
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surplus_env_num = len(ready_env_ids) - (n_episode - episode_count)
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if surplus_env_num > 0:
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mask = np.ones_like(ready_env_ids, dtype=bool)
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mask[env_ind_local[:surplus_env_num]] = False
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ready_env_ids = ready_env_ids[mask]
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self.data = self.data[mask]
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self.data.obs = self.data.obs_next
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if (n_step and step_count >= n_step) or (n_episode and episode_count >= n_episode):
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break
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# generate statistics
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self.collect_step += step_count
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self.collect_episode += episode_count
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collect_time = max(time.time() - start_time, 1e-9)
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self.collect_time += collect_time
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if n_episode:
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data = Batch(
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obs={},
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act={},
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rew={},
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terminated={},
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truncated={},
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done={},
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obs_next={},
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info={},
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policy={},
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)
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self.data = cast(RolloutBatchProtocol, data)
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self.reset_env()
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return CollectStats(
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n_collected_episodes=episode_count,
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n_collected_steps=step_count,
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collect_time=collect_time,
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collect_speed=step_count / collect_time,
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returns=np.array(episode_returns),
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returns_stat=SequenceSummaryStats.from_sequence(episode_returns)
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if len(episode_returns) > 0
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else None,
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lens=np.array(episode_lens, int),
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lens_stat=SequenceSummaryStats.from_sequence(episode_lens)
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if len(episode_lens) > 0
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else None,
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)
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class AsyncCollector(Collector):
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"""Async Collector handles async vector environment.
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The arguments are exactly the same as :class:`~tianshou.data.Collector`, please
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refer to :class:`~tianshou.data.Collector` for more detailed explanation.
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"""
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def __init__(
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self,
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policy: BasePolicy,
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env: BaseVectorEnv,
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buffer: ReplayBuffer | None = None,
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preprocess_fn: Callable[..., RolloutBatchProtocol] | None = None,
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exploration_noise: bool = False,
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) -> None:
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# assert env.is_async
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warnings.warn("Using async setting may collect extra transitions into buffer.")
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super().__init__(
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policy,
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env,
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buffer,
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preprocess_fn,
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exploration_noise,
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)
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def reset_env(self, gym_reset_kwargs: dict[str, Any] | None = None) -> None:
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super().reset_env(gym_reset_kwargs)
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self._ready_env_ids = np.arange(self.env_num)
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def collect(
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self,
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n_step: int | None = None,
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n_episode: int | None = None,
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random: bool = False,
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render: float | None = None,
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no_grad: bool = True,
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gym_reset_kwargs: dict[str, Any] | None = None,
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) -> CollectStats:
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"""Collect a specified number of step or episode with async env setting.
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This function doesn't collect exactly n_step or n_episode number of
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transitions. Instead, in order to support async setting, it may collect more
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than given n_step or n_episode transitions and save into buffer.
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:param n_step: how many steps you want to collect.
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:param n_episode: how many episodes you want to collect.
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:param random: whether to use random policy for collecting data. Default
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to False.
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:param render: the sleep time between rendering consecutive frames.
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Default to None (no rendering).
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:param no_grad: whether to retain gradient in policy.forward(). Default to
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True (no gradient retaining).
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:param gym_reset_kwargs: extra keyword arguments to pass into the environment's
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reset function. Defaults to None (extra keyword arguments)
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|
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.. note::
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One and only one collection number specification is permitted, either
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``n_step`` or ``n_episode``.
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:return: A dataclass object
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"""
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# collect at least n_step or n_episode
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if n_step is not None:
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assert n_episode is None, (
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"Only one of n_step or n_episode is allowed in Collector."
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f"collect, got n_step={n_step}, n_episode={n_episode}."
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)
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assert n_step > 0
|
|
elif n_episode is not None:
|
|
assert n_episode > 0
|
|
else:
|
|
raise TypeError(
|
|
"Please specify at least one (either n_step or n_episode) "
|
|
"in AsyncCollector.collect().",
|
|
)
|
|
|
|
ready_env_ids = self._ready_env_ids
|
|
|
|
start_time = time.time()
|
|
|
|
step_count = 0
|
|
episode_count = 0
|
|
episode_returns: list[float] = []
|
|
episode_lens: list[int] = []
|
|
episode_start_indices: list[int] = []
|
|
|
|
while True:
|
|
whole_data = self.data
|
|
self.data = self.data[ready_env_ids]
|
|
assert len(whole_data) == self.env_num # major difference
|
|
# restore the state: if the last state is None, it won't store
|
|
last_state = self.data.policy.pop("hidden_state", None)
|
|
|
|
# get the next action
|
|
if random:
|
|
try:
|
|
act_sample = [self._action_space[i].sample() for i in ready_env_ids]
|
|
except TypeError: # envpool's action space is not for per-env
|
|
act_sample = [self._action_space.sample() for _ in ready_env_ids]
|
|
act_sample = self.policy.map_action_inverse(act_sample) # type: ignore
|
|
self.data.update(act=act_sample)
|
|
else:
|
|
if no_grad:
|
|
with torch.no_grad(): # faster than retain_grad version
|
|
# self.data.obs will be used by agent to get result
|
|
result = self.policy(self.data, last_state)
|
|
else:
|
|
result = self.policy(self.data, last_state)
|
|
# update state / act / policy into self.data
|
|
policy = result.get("policy", Batch())
|
|
assert isinstance(policy, Batch)
|
|
state = result.get("state", None)
|
|
if state is not None:
|
|
policy.hidden_state = state # save state into buffer
|
|
act = to_numpy(result.act)
|
|
if self.exploration_noise:
|
|
act = self.policy.exploration_noise(act, self.data)
|
|
self.data.update(policy=policy, act=act)
|
|
|
|
# save act/policy before env.step
|
|
try:
|
|
whole_data.act[ready_env_ids] = self.data.act # type: ignore
|
|
whole_data.policy[ready_env_ids] = self.data.policy
|
|
except ValueError:
|
|
alloc_by_keys_diff(whole_data, self.data, self.env_num, False)
|
|
whole_data[ready_env_ids] = self.data # lots of overhead
|
|
|
|
# get bounded and remapped actions first (not saved into buffer)
|
|
action_remap = self.policy.map_action(self.data.act)
|
|
# step in env
|
|
obs_next, rew, terminated, truncated, info = self.env.step(
|
|
action_remap,
|
|
ready_env_ids,
|
|
)
|
|
done = np.logical_or(terminated, truncated)
|
|
|
|
# change self.data here because ready_env_ids has changed
|
|
try:
|
|
ready_env_ids = info["env_id"]
|
|
except Exception:
|
|
ready_env_ids = np.array([i["env_id"] for i in info])
|
|
self.data = whole_data[ready_env_ids]
|
|
|
|
self.data.update(
|
|
obs_next=obs_next,
|
|
rew=rew,
|
|
terminated=terminated,
|
|
truncated=truncated,
|
|
info=info,
|
|
)
|
|
if self.preprocess_fn:
|
|
try:
|
|
self.data.update(
|
|
self.preprocess_fn(
|
|
obs_next=self.data.obs_next,
|
|
rew=self.data.rew,
|
|
terminated=self.data.terminated,
|
|
truncated=self.data.truncated,
|
|
info=self.data.info,
|
|
env_id=ready_env_ids,
|
|
act=self.data.act,
|
|
),
|
|
)
|
|
except TypeError:
|
|
self.data.update(
|
|
self.preprocess_fn(
|
|
obs_next=self.data.obs_next,
|
|
rew=self.data.rew,
|
|
done=self.data.done,
|
|
info=self.data.info,
|
|
env_id=ready_env_ids,
|
|
act=self.data.act,
|
|
),
|
|
)
|
|
|
|
if render:
|
|
self.env.render()
|
|
if render > 0 and not np.isclose(render, 0):
|
|
time.sleep(render)
|
|
|
|
# add data into the buffer
|
|
ptr, ep_rew, ep_len, ep_idx = self.buffer.add(self.data, buffer_ids=ready_env_ids)
|
|
|
|
# collect statistics
|
|
step_count += len(ready_env_ids)
|
|
|
|
if np.any(done):
|
|
env_ind_local = np.where(done)[0]
|
|
env_ind_global = ready_env_ids[env_ind_local]
|
|
episode_count += len(env_ind_local)
|
|
episode_lens.extend(ep_len[env_ind_local])
|
|
episode_returns.extend(ep_rew[env_ind_local])
|
|
episode_start_indices.extend(ep_idx[env_ind_local])
|
|
# now we copy obs_next to obs, but since there might be
|
|
# finished episodes, we have to reset finished envs first.
|
|
self._reset_env_with_ids(env_ind_local, env_ind_global, gym_reset_kwargs)
|
|
for i in env_ind_local:
|
|
self._reset_state(i)
|
|
|
|
try:
|
|
# Need to ignore types b/c according to mypy Tensors cannot be indexed
|
|
# by arrays (which they can...)
|
|
whole_data.obs[ready_env_ids] = self.data.obs_next # type: ignore
|
|
whole_data.rew[ready_env_ids] = self.data.rew
|
|
whole_data.done[ready_env_ids] = self.data.done
|
|
whole_data.info[ready_env_ids] = self.data.info # type: ignore
|
|
except ValueError:
|
|
alloc_by_keys_diff(whole_data, self.data, self.env_num, False)
|
|
self.data.obs = self.data.obs_next
|
|
# lots of overhead
|
|
whole_data[ready_env_ids] = self.data
|
|
self.data = whole_data
|
|
|
|
if (n_step and step_count >= n_step) or (n_episode and episode_count >= n_episode):
|
|
break
|
|
|
|
self._ready_env_ids = ready_env_ids
|
|
|
|
# generate statistics
|
|
self.collect_step += step_count
|
|
self.collect_episode += episode_count
|
|
collect_time = max(time.time() - start_time, 1e-9)
|
|
self.collect_time += collect_time
|
|
|
|
return CollectStats(
|
|
n_collected_episodes=episode_count,
|
|
n_collected_steps=step_count,
|
|
collect_time=collect_time,
|
|
collect_speed=step_count / collect_time,
|
|
returns=np.array(episode_returns),
|
|
returns_stat=SequenceSummaryStats.from_sequence(episode_returns)
|
|
if len(episode_returns) > 0
|
|
else None,
|
|
lens=np.array(episode_lens, int),
|
|
lens_stat=SequenceSummaryStats.from_sequence(episode_lens)
|
|
if len(episode_lens) > 0
|
|
else None,
|
|
)
|