import gym
import time
import torch
import warnings
import numpy as np
from typing import Any, Dict, List, Union, Optional, Callable
from tianshou.policy import BasePolicy
from tianshou.data.batch import _alloc_by_keys_diff
from tianshou.env import BaseVectorEnv, DummyVectorEnv
from tianshou.data import (
Batch,
ReplayBuffer,
ReplayBufferManager,
VectorReplayBuffer,
CachedReplayBuffer,
to_numpy,
)
[docs]class Collector(object):
"""Collector enables the policy to interact with different types of envs with \
exact number of steps or episodes.
:param policy: an instance of the :class:`~tianshou.policy.BasePolicy` class.
:param env: a ``gym.Env`` environment or an instance of the
:class:`~tianshou.env.BaseVectorEnv` class.
:param buffer: an instance of the :class:`~tianshou.data.ReplayBuffer` class.
If set to None, it will not store the data. Default to None.
:param function preprocess_fn: a function called before the data has been added to
the buffer, see issue #42 and :ref:`preprocess_fn`. Default to None.
:param bool exploration_noise: determine whether the action needs to be modified
with corresponding policy's exploration noise. If so, "policy.
exploration_noise(act, batch)" will be called automatically to add the
exploration noise into action. Default to False.
The "preprocess_fn" is a function called before the data has been added to the
buffer with batch format. It will receive with only "obs" when the collector resets
the environment, and will receive four keys "obs_next", "rew", "done", "info" in a
normal env step. It returns either a dict or a :class:`~tianshou.data.Batch` with
the modified keys and values. Examples are in "test/base/test_collector.py".
.. note::
Please make sure the given environment has a time limitation if using n_episode
collect option.
"""
def __init__(
self,
policy: BasePolicy,
env: Union[gym.Env, BaseVectorEnv],
buffer: Optional[ReplayBuffer] = None,
preprocess_fn: Optional[Callable[..., Batch]] = None,
exploration_noise: bool = False,
) -> None:
super().__init__()
if not isinstance(env, BaseVectorEnv):
env = DummyVectorEnv([lambda: env])
self.env = env
self.env_num = len(env)
self.exploration_noise = exploration_noise
self._assign_buffer(buffer)
self.policy = policy
self.preprocess_fn = preprocess_fn
self._action_space = env.action_space
# avoid creating attribute outside __init__
self.reset()
def _assign_buffer(self, buffer: Optional[ReplayBuffer]) -> None:
"""Check if the buffer matches the constraint."""
if buffer is None:
buffer = VectorReplayBuffer(self.env_num, self.env_num)
elif isinstance(buffer, ReplayBufferManager):
assert buffer.buffer_num >= self.env_num
if isinstance(buffer, CachedReplayBuffer):
assert buffer.cached_buffer_num >= self.env_num
else: # ReplayBuffer or PrioritizedReplayBuffer
assert buffer.maxsize > 0
if self.env_num > 1:
if type(buffer) == ReplayBuffer:
buffer_type = "ReplayBuffer"
vector_type = "VectorReplayBuffer"
else:
buffer_type = "PrioritizedReplayBuffer"
vector_type = "PrioritizedVectorReplayBuffer"
raise TypeError(
f"Cannot use {buffer_type}(size={buffer.maxsize}, ...) to collect "
f"{self.env_num} envs,\n\tplease use {vector_type}(total_size="
f"{buffer.maxsize}, buffer_num={self.env_num}, ...) instead."
)
self.buffer = buffer
[docs] def reset(self) -> None:
"""Reset all related variables in the collector."""
# use empty Batch for "state" so that self.data supports slicing
# convert empty Batch to None when passing data to policy
self.data = Batch(obs={}, act={}, rew={}, done={},
obs_next={}, info={}, policy={})
self.reset_env()
self.reset_buffer()
self.reset_stat()
[docs] def reset_stat(self) -> None:
"""Reset the statistic variables."""
self.collect_step, self.collect_episode, self.collect_time = 0, 0, 0.0
[docs] def reset_buffer(self, keep_statistics: bool = False) -> None:
"""Reset the data buffer."""
self.buffer.reset(keep_statistics=keep_statistics)
[docs] def reset_env(self) -> None:
"""Reset all of the environments."""
obs = self.env.reset()
if self.preprocess_fn:
obs = self.preprocess_fn(obs=obs).get("obs", obs)
self.data.obs = obs
def _reset_state(self, id: Union[int, List[int]]) -> None:
"""Reset the hidden state: self.data.state[id]."""
if hasattr(self.data.policy, "hidden_state"):
state = self.data.policy.hidden_state # it is a reference
if isinstance(state, torch.Tensor):
state[id].zero_()
elif isinstance(state, np.ndarray):
state[id] = None if state.dtype == object else 0
elif isinstance(state, Batch):
state.empty_(id)
[docs] def collect(
self,
n_step: Optional[int] = None,
n_episode: Optional[int] = None,
random: bool = False,
render: Optional[float] = None,
no_grad: bool = True,
) -> Dict[str, Any]:
"""Collect a specified number of step or episode.
To ensure unbiased sampling result with n_episode option, this function will
first collect ``n_episode - env_num`` episodes, then for the last ``env_num``
episodes, they will be collected evenly from each env.
:param int n_step: how many steps you want to collect.
:param int n_episode: how many episodes you want to collect.
:param bool random: whether to use random policy for collecting data. Default
to False.
:param float render: the sleep time between rendering consecutive frames.
Default to None (no rendering).
:param bool no_grad: whether to retain gradient in policy.forward(). Default to
True (no gradient retaining).
.. note::
One and only one collection number specification is permitted, either
``n_step`` or ``n_episode``.
:return: A dict including the following keys
* ``n/ep`` collected number of episodes.
* ``n/st`` collected number of steps.
* ``rews`` array of episode reward over collected episodes.
* ``lens`` array of episode length over collected episodes.
* ``idxs`` array of episode start index in buffer over collected episodes.
"""
assert not self.env.is_async, "Please use AsyncCollector if using async venv."
if n_step is not None:
assert n_episode is None, (
f"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
if not n_step % self.env_num == 0:
warnings.warn(
f"n_step={n_step} is not a multiple of #env ({self.env_num}), "
"which may cause extra transitions collected into the buffer."
)
ready_env_ids = np.arange(self.env_num)
elif n_episode is not None:
assert n_episode > 0
ready_env_ids = np.arange(min(self.env_num, n_episode))
self.data = self.data[:min(self.env_num, n_episode)]
else:
raise TypeError("Please specify at least one (either n_step or n_episode) "
"in AsyncCollector.collect().")
start_time = time.time()
step_count = 0
episode_count = 0
episode_rews = []
episode_lens = []
episode_start_indices = []
while True:
assert len(self.data) == len(ready_env_ids)
# 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:
self.data.update(
act=[self._action_space[i].sample() for i in ready_env_ids])
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)
# 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, done, info = self.env.step(action_remap, id=ready_env_ids)
self.data.update(obs_next=obs_next, rew=rew, done=done, info=info)
if self.preprocess_fn:
self.data.update(self.preprocess_fn(
obs_next=self.data.obs_next,
rew=self.data.rew,
done=self.data.done,
info=self.data.info,
))
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.
obs_reset = self.env.reset(env_ind_global)
if self.preprocess_fn:
obs_reset = self.preprocess_fn(obs=obs_reset).get("obs", obs_reset)
self.data.obs_next[env_ind_local] = obs_reset
for i in env_ind_local:
self._reset_state(i)
# remove surplus env id from ready_env_ids
# to avoid bias in selecting environments
if n_episode:
surplus_env_num = len(ready_env_ids) - (n_episode - episode_count)
if surplus_env_num > 0:
mask = np.ones_like(ready_env_ids, dtype=bool)
mask[env_ind_local[:surplus_env_num]] = False
ready_env_ids = ready_env_ids[mask]
self.data = self.data[mask]
self.data.obs = self.data.obs_next
if (n_step and step_count >= n_step) or \
(n_episode and episode_count >= n_episode):
break
# generate statistics
self.collect_step += step_count
self.collect_episode += episode_count
self.collect_time += max(time.time() - start_time, 1e-9)
if n_episode:
self.data = Batch(obs={}, act={}, rew={}, done={},
obs_next={}, info={}, policy={})
self.reset_env()
if episode_count > 0:
rews, lens, idxs = list(map(
np.concatenate, [episode_rews, episode_lens, episode_start_indices]))
else:
rews, lens, idxs = np.array([]), np.array([], int), np.array([], int)
return {
"n/ep": episode_count,
"n/st": step_count,
"rews": rews,
"lens": lens,
"idxs": idxs,
}
[docs]class AsyncCollector(Collector):
"""Async Collector handles async vector environment.
The arguments are exactly the same as :class:`~tianshou.data.Collector`, please
refer to :class:`~tianshou.data.Collector` for more detailed explanation.
"""
def __init__(
self,
policy: BasePolicy,
env: BaseVectorEnv,
buffer: Optional[ReplayBuffer] = None,
preprocess_fn: Optional[Callable[..., Batch]] = None,
exploration_noise: bool = False,
) -> None:
assert env.is_async
super().__init__(policy, env, buffer, preprocess_fn, exploration_noise)
[docs] def reset_env(self) -> None:
super().reset_env()
self._ready_env_ids = np.arange(self.env_num)
[docs] def collect(
self,
n_step: Optional[int] = None,
n_episode: Optional[int] = None,
random: bool = False,
render: Optional[float] = None,
no_grad: bool = True,
) -> Dict[str, Any]:
"""Collect a specified number of step or episode with async env setting.
This function doesn't collect exactly n_step or n_episode number of
transitions. Instead, in order to support async setting, it may collect more
than given n_step or n_episode transitions and save into buffer.
:param int n_step: how many steps you want to collect.
:param int n_episode: how many episodes you want to collect.
:param bool random: whether to use random policy for collecting data. Default
to False.
:param float render: the sleep time between rendering consecutive frames.
Default to None (no rendering).
:param bool no_grad: whether to retain gradient in policy.forward(). Default to
True (no gradient retaining).
.. note::
One and only one collection number specification is permitted, either
``n_step`` or ``n_episode``.
:return: A dict including the following keys
* ``n/ep`` collected number of episodes.
* ``n/st`` collected number of steps.
* ``rews`` array of episode reward over collected episodes.
* ``lens`` array of episode length over collected episodes.
* ``idxs`` array of episode start index in buffer over collected episodes.
"""
# 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().")
warnings.warn("Using async setting may collect extra transitions into buffer.")
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:
self.data.update(
act=[self._action_space[i].sample() for i in ready_env_ids])
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
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, done, info = self.env.step(action_remap, id=ready_env_ids)
# change self.data here because ready_env_ids has changed
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, done=done, info=info)
if self.preprocess_fn:
self.data.update(self.preprocess_fn(
obs_next=self.data.obs_next,
rew=self.data.rew,
done=self.data.done,
info=self.data.info,
))
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.
obs_reset = self.env.reset(env_ind_global)
if self.preprocess_fn:
obs_reset = self.preprocess_fn(obs=obs_reset).get("obs", obs_reset)
self.data.obs_next[env_ind_local] = obs_reset
for i in env_ind_local:
self._reset_state(i)
try:
whole_data.obs[ready_env_ids] = self.data.obs_next
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
except ValueError:
_alloc_by_keys_diff(whole_data, self.data, self.env_num, False)
self.data.obs = self.data.obs_next
whole_data[ready_env_ids] = self.data # lots of overhead
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]))
else:
rews, lens, idxs = np.array([]), np.array([], int), np.array([], int)
return {
"n/ep": episode_count,
"n/st": step_count,
"rews": rews,
"lens": lens,
"idxs": idxs,
}