import time
import warnings
from typing import Any, Callable, Dict, List, Optional, Union
import gymnasium as gym
import numpy as np
import torch
from tianshou.data import (
Batch,
ReplayBuffer,
ReplayBufferManager,
VectorReplayBuffer,
to_numpy,
)
from tianshou.env import BaseVectorEnv, DummyVectorEnv
from fsrl.policy import BasePolicy
[docs]class FastCollector(object):
"""Collector enables the policy to interact with different types of envs with \
exact number of episodes.
This collector is a simplified version of Tianshou's `collector
<https://tianshou.readthedocs.io/en/master/api/tianshou.data.html#collector>`_, so
it is safe to check their documentation for details. The main change is the \
support to extract the cost signals from the interaction data.
:param policy: an instance of the :class:`~fsrl.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. 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.
.. note::
Please make sure the given environment has a time limitation (can be done), \
because we only support the `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 isinstance(env, gym.Env) and not hasattr(env, "__len__"):
warnings.warn("Single environment detected, wrap to DummyVectorEnv.")
self.env = DummyVectorEnv([lambda: env])
else:
self.env = env
self.env_num = len(self.env)
self.exploration_noise = exploration_noise
self._assign_buffer(buffer)
self.policy = policy
self.preprocess_fn = preprocess_fn
self._action_space = self.env.action_space
# avoid creating attribute outside __init__
self.reset(False)
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
else: # ReplayBuffer or PrioritizedReplayBuffer
assert buffer.maxsize > 0
if self.env_num > 1:
if isinstance(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,
reset_buffer: bool = True,
gym_reset_kwargs: Optional[Dict[str, Any]] = None,
) -> None:
"""Reset the environment, statistics, current data and possibly replay memory.
:param bool reset_buffer: if true, reset the replay buffer that is attached to
the collector.
:param gym_reset_kwargs: extra keyword arguments to pass into the environment's
reset function. Defaults to None (extra keyword arguments)
"""
# 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={},
cost={},
terminated={},
truncated={},
done={},
obs_next={},
info={},
policy={}
)
self.reset_env(gym_reset_kwargs)
if reset_buffer:
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, gym_reset_kwargs: Optional[Dict[str, Any]] = None) -> None:
"""Reset all of the environments."""
gym_reset_kwargs = gym_reset_kwargs if gym_reset_kwargs else {}
rval = self.env.reset(**gym_reset_kwargs)
returns_info = isinstance(rval, (tuple, list)) and len(rval) == 2 and (
isinstance(rval[1], dict) or isinstance(rval[1][0], dict)
)
if returns_info:
obs, info = rval
if self.preprocess_fn:
processed_data = self.preprocess_fn(
obs=obs, info=info, env_id=np.arange(self.env_num)
)
obs = processed_data.get("obs", obs)
info = processed_data.get("info", info)
self.data.info = info
else:
obs = rval
if self.preprocess_fn:
obs = self.preprocess_fn(obs=obs,
env_id=np.arange(self.env_num)).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)
def _reset_env_with_ids(
self,
local_ids: Union[List[int], np.ndarray],
global_ids: Union[List[int], np.ndarray],
gym_reset_kwargs: Optional[Dict[str, Any]] = None,
) -> None:
gym_reset_kwargs = gym_reset_kwargs if gym_reset_kwargs else {}
rval = self.env.reset(global_ids, **gym_reset_kwargs)
returns_info = isinstance(rval, (tuple, list)) and len(rval) == 2 and (
isinstance(rval[1], dict) or isinstance(rval[1][0], dict)
)
if returns_info:
obs_reset, info = rval
if self.preprocess_fn:
processed_data = self.preprocess_fn(
obs=obs_reset, info=info, env_id=global_ids
)
obs_reset = processed_data.get("obs", obs_reset)
info = processed_data.get("info", info)
self.data.info[local_ids] = info
else:
obs_reset = rval
if self.preprocess_fn:
obs_reset = self.preprocess_fn(obs=obs_reset,
env_id=global_ids).get("obs", obs_reset)
self.data.obs_next[local_ids] = obs_reset
[docs] def collect(
self,
n_episode: int = 1,
random: bool = False,
render: bool = False,
no_grad: bool = True,
gym_reset_kwargs: Optional[Dict[str, Any]] = None,
) -> 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_episode: how many episodes you want to collect.
:param bool random: whether to use random policy for collecting data. Default to
False.
:param bool render: Whether to render the environment during evaluation, defaults
to False
:param bool no_grad: whether to retain gradient in policy.forward(). Default to
True (no gradient retaining).
:param gym_reset_kwargs: extra keyword arguments to pass into the environment's
reset function. Defaults to None (extra keyword arguments)
.. note::
We don not support the `n_step` collection method in Tianshou, because using
`n_episode` only can facilitate the episodic cost computation and better
evaluate the agent.
:return: A dict including the following keys
* ``n/ep`` collected number of episodes.
* ``n/st`` collected number of steps.
* ``rew`` mean of episodic rewards.
* ``len`` mean of episodic lengths.
* ``total_cost`` cumulative costs in this collect.
* ``cost`` mean of episodic costs.
* ``truncated`` mean of episodic truncation.
* ``terminated`` mean of episodic termination.
"""
if 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 n_episode"
"in FastCollector.collect().")
start_time = time.time()
step_count = 0
total_cost = 0
termination_count = 0
truncation_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:
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)
# get bounded and remapped actions first (not saved into buffer)
action_remap = self.policy.map_action(self.data.act)
# step in env
result = self.env.step(action_remap, ready_env_ids)
if len(result) == 5:
obs_next, rew, terminated, truncated, info = result
done = np.logical_or(terminated, truncated)
elif len(result) == 4:
obs_next, rew, done, info = result
if isinstance(info, dict):
truncated = info["TimeLimit.truncated"]
else:
truncated = np.array(
[
info_item.get("TimeLimit.truncated", False)
for info_item in info
]
)
terminated = np.logical_and(done, ~truncated)
else:
raise ValueError()
self.data.update(
obs_next=obs_next,
rew=rew,
terminated=terminated,
truncated=truncated,
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,
policy=self.data.policy,
env_id=ready_env_ids,
)
)
cost = self.data.info.get("cost", np.zeros(rew.shape))
total_cost += np.sum(cost)
self.data.update(cost=cost)
if render:
self.env.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])
termination_count += np.sum(terminated)
truncation_count += np.sum(truncated)
# 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)
# 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_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={},
cost={},
terminated={},
truncated={},
done={},
obs_next={},
info={},
policy={}
)
self.reset_env()
if episode_count > 0:
rews, lens = list(map(np.concatenate, [episode_rews, episode_lens]))
rew_mean = rews.mean()
len_mean = lens.mean()
else:
rew_mean = len_mean = 0
done_count = termination_count + truncation_count
return {
"n/ep": episode_count,
"n/st": step_count,
"rew": rew_mean,
"len": len_mean,
"total_cost": total_cost,
"cost": total_cost / episode_count,
"truncated": truncation_count / done_count,
"terminated": termination_count / done_count,
}