2cc34fb72b
Closes #914 Additional changes: - Deprecate python below 11 - Remove 3rd party and throughput tests. This simplifies install and test pipeline - Remove gym compatibility and shimmy - Format with 3.11 conventions. In particular, add `zip(..., strict=True/False)` where possible Since the additional tests and gym were complicating the CI pipeline (flaky and dist-dependent), it didn't make sense to work on fixing the current tests in this PR to then just delete them in the next one. So this PR changes the build and removes these tests at the same time.
659 lines
27 KiB
Python
659 lines
27 KiB
Python
import time
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import warnings
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from collections.abc import Callable
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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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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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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 bool 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[..., Batch] | 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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warnings.warn("Single environment detected, wrap to DummyVectorEnv.")
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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 bool 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
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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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) -> dict[str, Any]:
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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 int n_step: how many steps you want to collect.
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:param int n_episode: how many episodes you want to collect.
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:param bool random: whether to use random policy for collecting data. Default
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to False.
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:param float render: the sleep time between rendering consecutive frames.
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Default to None (no rendering).
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:param bool 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 dict including the following keys
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* ``n/ep`` collected number of episodes.
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* ``n/st`` collected number of steps.
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* ``rews`` array of episode reward over collected episodes.
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* ``lens`` array of episode length over collected episodes.
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* ``idxs`` array of episode start index in buffer over collected episodes.
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* ``rew`` mean of episodic rewards.
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* ``len`` mean of episodic lengths.
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* ``rew_std`` standard error of episodic rewards.
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* ``len_std`` standard error of episodic lengths.
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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_rews = []
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episode_lens = []
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episode_start_indices = []
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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.append(ep_len[env_ind_local])
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episode_rews.append(ep_rew[env_ind_local])
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episode_start_indices.append(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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self.collect_time += max(time.time() - start_time, 1e-9)
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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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if episode_count > 0:
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rews, lens, idxs = list(
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map(np.concatenate, [episode_rews, episode_lens, episode_start_indices]),
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)
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rew_mean, rew_std = rews.mean(), rews.std()
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len_mean, len_std = lens.mean(), lens.std()
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else:
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rews, lens, idxs = np.array([]), np.array([], int), np.array([], int)
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rew_mean = rew_std = len_mean = len_std = 0
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return {
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"n/ep": episode_count,
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"n/st": step_count,
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"rews": rews,
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"lens": lens,
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"idxs": idxs,
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"rew": rew_mean,
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"len": len_mean,
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"rew_std": rew_std,
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"len_std": len_std,
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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[..., Batch] | 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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) -> dict[str, Any]:
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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 int n_step: how many steps you want to collect.
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:param int n_episode: how many episodes you want to collect.
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:param bool random: whether to use random policy for collecting data. Default
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to False.
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:param float render: the sleep time between rendering consecutive frames.
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Default to None (no rendering).
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:param bool 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 dict including the following keys
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* ``n/ep`` collected number of episodes.
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* ``n/st`` collected number of steps.
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* ``rews`` array of episode reward over collected episodes.
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* ``lens`` array of episode length over collected episodes.
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* ``idxs`` array of episode start index in buffer over collected episodes.
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* ``rew`` mean of episodic rewards.
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* ``len`` mean of episodic lengths.
|
|
* ``rew_std`` standard error of episodic rewards.
|
|
* ``len_std`` standard error of episodic lengths.
|
|
"""
|
|
# collect at least n_step or n_episode
|
|
if n_step is not None:
|
|
assert n_episode is None, (
|
|
"Only one of n_step or n_episode is allowed in Collector."
|
|
f"collect, got n_step={n_step}, n_episode={n_episode}."
|
|
)
|
|
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_rews = []
|
|
episode_lens = []
|
|
episode_start_indices = []
|
|
|
|
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.append(ep_len[env_ind_local])
|
|
episode_rews.append(ep_rew[env_ind_local])
|
|
episode_start_indices.append(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
|
|
self.collect_time += max(time.time() - start_time, 1e-9)
|
|
|
|
if episode_count > 0:
|
|
rews, lens, idxs = list(
|
|
map(np.concatenate, [episode_rews, episode_lens, episode_start_indices]),
|
|
)
|
|
rew_mean, rew_std = rews.mean(), rews.std()
|
|
len_mean, len_std = lens.mean(), lens.std()
|
|
else:
|
|
rews, lens, idxs = np.array([]), np.array([], int), np.array([], int)
|
|
rew_mean = rew_std = len_mean = len_std = 0
|
|
|
|
return {
|
|
"n/ep": episode_count,
|
|
"n/st": step_count,
|
|
"rews": rews,
|
|
"lens": lens,
|
|
"idxs": idxs,
|
|
"rew": rew_mean,
|
|
"len": len_mean,
|
|
"rew_std": rew_std,
|
|
"len_std": len_std,
|
|
}
|