import gym
import time
import torch
import warnings
import numpy as np
from typing import Any, Dict, List, Union, Optional, Callable
from tianshou.utils import MovAvg
from tianshou.env import BaseVectorEnv
from tianshou.policy import BasePolicy
from tianshou.exploration import BaseNoise
from tianshou.data import Batch, ReplayBuffer, ListReplayBuffer, to_numpy
[docs]class Collector(object):
"""The :class:`~tianshou.data.Collector` enables the policy to interact
with different types of environments conveniently.
: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, or a list of :class:`~tianshou.data.ReplayBuffer`. If set to
``None``, it will automatically assign a small-size
:class:`~tianshou.data.ReplayBuffer`.
:param function preprocess_fn: a function called before the data has been
added to the buffer, see issue #42 and :ref:`preprocess_fn`, defaults
to ``None``.
:param int stat_size: for the moving average of recording speed, defaults
to 100.
:param BaseNoise action_noise: add a noise to continuous action. Normally
a policy already has a noise param for exploration in training phase,
so this is recommended to use in test collector for some purpose.
:param function reward_metric: to be used in multi-agent RL. The reward to
report is of shape [agent_num], but we need to return a single scalar
to monitor training. This function specifies what is the desired
metric, e.g., the reward of agent 1 or the average reward over all
agents. By default, the behavior is to select the reward of agent 1.
The ``preprocess_fn`` is a function called before the data has been added
to the buffer with batch format, which receives up to 7 keys as listed in
:class:`~tianshou.data.Batch`. It will receive with only ``obs`` when the
collector resets the environment. 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".
Example:
::
policy = PGPolicy(...) # or other policies if you wish
env = gym.make('CartPole-v0')
replay_buffer = ReplayBuffer(size=10000)
# here we set up a collector with a single environment
collector = Collector(policy, env, buffer=replay_buffer)
# the collector supports vectorized environments as well
envs = VectorEnv([lambda: gym.make('CartPole-v0') for _ in range(3)])
buffers = [ReplayBuffer(size=5000) for _ in range(3)]
# you can also pass a list of replay buffer to collector, for multi-env
# collector = Collector(policy, envs, buffer=buffers)
collector = Collector(policy, envs, buffer=replay_buffer)
# collect at least 3 episodes
collector.collect(n_episode=3)
# collect 1 episode for the first env, 3 for the third env
collector.collect(n_episode=[1, 0, 3])
# collect at least 2 steps
collector.collect(n_step=2)
# collect episodes with visual rendering (the render argument is the
# sleep time between rendering consecutive frames)
collector.collect(n_episode=1, render=0.03)
# sample data with a given number of batch-size:
batch_data = collector.sample(batch_size=64)
# policy.learn(batch_data) # btw, vanilla policy gradient only
# supports on-policy training, so here we pick all data in the buffer
batch_data = collector.sample(batch_size=0)
policy.learn(batch_data)
# on-policy algorithms use the collected data only once, so here we
# clear the buffer
collector.reset_buffer()
For the scenario of collecting data from multiple environments to a single
buffer, the cache buffers will turn on automatically. It may return the
data more than the given limitation.
.. note::
Please make sure the given environment has a time limitation.
"""
def __init__(self,
policy: BasePolicy,
env: Union[gym.Env, BaseVectorEnv],
buffer: Optional[ReplayBuffer] = None,
preprocess_fn: Callable[[Any], Union[dict, Batch]] = None,
stat_size: Optional[int] = 100,
action_noise: Optional[BaseNoise] = None,
reward_metric: Optional[Callable[[np.ndarray], float]] = None,
**kwargs) -> None:
super().__init__()
self.env = env
self.env_num = 1
self.collect_time, self.collect_step, self.collect_episode = 0., 0, 0
self.buffer = buffer
self.policy = policy
self.preprocess_fn = preprocess_fn
self.process_fn = policy.process_fn
self._multi_env = isinstance(env, BaseVectorEnv)
# need multiple cache buffers only if storing in one buffer
self._cached_buf = []
if self._multi_env:
self.env_num = len(env)
self._cached_buf = [ListReplayBuffer()
for _ in range(self.env_num)]
self.stat_size = stat_size
self._action_noise = action_noise
self._rew_metric = reward_metric or Collector._default_rew_metric
self.reset()
@staticmethod
def _default_rew_metric(x):
# this internal function is designed for single-agent RL
# for multi-agent RL, a reward_metric must be provided
assert np.asanyarray(x).size == 1, \
'Please specify the reward_metric ' \
'since the reward is not a scalar.'
return x
[docs] def reset(self) -> None:
"""Reset all related variables in the collector."""
self.data = Batch(state={}, obs={}, act={}, rew={}, done={}, info={},
obs_next={}, policy={})
self.reset_env()
self.reset_buffer()
self.step_speed = MovAvg(self.stat_size)
self.episode_speed = MovAvg(self.stat_size)
self.collect_time, self.collect_step, self.collect_episode = 0., 0, 0
if self._action_noise is not None:
self._action_noise.reset()
[docs] def reset_buffer(self) -> None:
"""Reset the main data buffer."""
if self.buffer is not None:
self.buffer.reset()
[docs] def get_env_num(self) -> int:
"""Return the number of environments the collector have."""
return self.env_num
[docs] def reset_env(self) -> None:
"""Reset all of the environment(s)' states and reset all of the cache
buffers (if need).
"""
obs = self.env.reset()
if not self._multi_env:
obs = self._make_batch(obs)
if self.preprocess_fn:
obs = self.preprocess_fn(obs=obs).get('obs', obs)
self.data.obs = obs
self.reward = 0. # will be specified when the first data is ready
self.length = np.zeros(self.env_num)
for b in self._cached_buf:
b.reset()
[docs] def seed(self, seed: Optional[Union[int, List[int]]] = None) -> None:
"""Reset all the seed(s) of the given environment(s)."""
return self.env.seed(seed)
[docs] def render(self, **kwargs) -> None:
"""Render all the environment(s)."""
return self.env.render(**kwargs)
[docs] def close(self) -> None:
"""Close the environment(s)."""
self.env.close()
def _make_batch(self, data: Any) -> np.ndarray:
"""Return [data]."""
if isinstance(data, np.ndarray):
return data[None]
else:
return np.array([data])
def _reset_state(self, id: Union[int, List[int]]) -> None:
"""Reset self.data.state[id]."""
state = self.data.state # it is a reference
if isinstance(state, torch.Tensor):
state[id].zero_()
elif isinstance(state, np.ndarray):
state[id] = None if state.dtype == np.object else 0
elif isinstance(state, Batch):
state.empty_(id)
[docs] def collect(self,
n_step: int = 0,
n_episode: Union[int, List[int]] = 0,
random: bool = False,
render: Optional[float] = None,
log_fn: Optional[Callable[[dict], None]] = None
) -> Dict[str, float]:
"""Collect a specified number of step or episode.
:param int n_step: how many steps you want to collect.
:param n_episode: how many episodes you want to collect (in each
environment).
:type n_episode: int or list
:param bool random: whether to use random policy for collecting data,
defaults to ``False``.
:param float render: the sleep time between rendering consecutive
frames, defaults to ``None`` (no rendering).
:param function log_fn: a function which receives env info, typically
for tensorboard logging.
.. 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`` the collected number of episodes.
* ``n/st`` the collected number of steps.
* ``v/st`` the speed of steps per second.
* ``v/ep`` the speed of episode per second.
* ``rew`` the mean reward over collected episodes.
* ``len`` the mean length over collected episodes.
"""
if not self._multi_env:
n_episode = np.sum(n_episode)
start_time = time.time()
assert sum([(n_step != 0), (n_episode != 0)]) == 1, \
"One and only one collection number specification is permitted!"
cur_step, cur_episode = 0, np.zeros(self.env_num)
reward_sum, length_sum = 0., 0
while True:
if cur_step >= 100000 and cur_episode.sum() == 0:
warnings.warn(
'There are already many steps in an episode. '
'You should add a time limitation to your environment!',
Warning)
# restore the state and the input data
last_state = self.data.state
if last_state.is_empty():
last_state = None
self.data.update(state=Batch(), obs_next=Batch(), policy=Batch())
# calculate the next action
if random:
action_space = self.env.action_space
if isinstance(action_space, list):
result = Batch(act=[a.sample() for a in action_space])
else:
result = Batch(act=self._make_batch(action_space.sample()))
else:
with torch.no_grad():
result = self.policy(self.data, last_state)
# convert None to Batch(), since None is reserved for 0-init
state = result.get('state', Batch())
if state is None:
state = Batch()
self.data.state = state
if hasattr(result, 'policy'):
self.data.policy = to_numpy(result.policy)
# save hidden state to policy._state, in order to save into buffer
self.data.policy._state = self.data.state
self.data.act = to_numpy(result.act)
if self._action_noise is not None:
self.data.act += self._action_noise(self.data.act.shape)
# step in env
obs_next, rew, done, info = self.env.step(
self.data.act if self._multi_env else self.data.act[0])
# move data to self.data
if not self._multi_env:
obs_next = self._make_batch(obs_next)
rew = self._make_batch(rew)
done = self._make_batch(done)
info = self._make_batch(info)
self.data.obs_next = obs_next
self.data.rew = rew
self.data.done = done
self.data.info = info
if log_fn:
log_fn(info if self._multi_env else info[0])
if render:
self.render()
if render > 0:
time.sleep(render)
# add data into the buffer
self.length += 1
self.reward += self.data.rew
if self.preprocess_fn:
result = self.preprocess_fn(**self.data)
self.data.update(result)
if self._multi_env: # cache_buffer branch
for i in range(self.env_num):
self._cached_buf[i].add(**self.data[i])
if self.data.done[i]:
if n_step != 0 or np.isscalar(n_episode) or \
cur_episode[i] < n_episode[i]:
cur_episode[i] += 1
reward_sum += self.reward[i]
length_sum += self.length[i]
if self._cached_buf:
cur_step += len(self._cached_buf[i])
if self.buffer is not None:
self.buffer.update(self._cached_buf[i])
self.reward[i], self.length[i] = 0., 0
if self._cached_buf:
self._cached_buf[i].reset()
self._reset_state(i)
obs_next = self.data.obs_next
if sum(self.data.done):
obs_next = self.env.reset(np.where(self.data.done)[0])
if self.preprocess_fn:
obs_next = self.preprocess_fn(obs=obs_next).get(
'obs', obs_next)
self.data.obs_next = obs_next
if n_episode != 0:
if isinstance(n_episode, list) and \
(cur_episode >= np.array(n_episode)).all() or \
np.isscalar(n_episode) and \
cur_episode.sum() >= n_episode:
break
else: # single buffer, without cache_buffer
if self.buffer is not None:
self.buffer.add(**self.data[0])
cur_step += 1
if self.data.done[0]:
cur_episode += 1
reward_sum += self.reward[0]
length_sum += self.length[0]
self.reward, self.length = 0., np.zeros(self.env_num)
self.data.state = Batch()
obs_next = self._make_batch(self.env.reset())
if self.preprocess_fn:
obs_next = self.preprocess_fn(obs=obs_next).get(
'obs', obs_next)
self.data.obs_next = obs_next
if n_episode != 0 and cur_episode >= n_episode:
break
if n_step != 0 and cur_step >= n_step:
break
self.data.obs = self.data.obs_next
self.data.obs = self.data.obs_next
# generate the statistics
cur_episode = sum(cur_episode)
duration = max(time.time() - start_time, 1e-9)
self.step_speed.add(cur_step / duration)
self.episode_speed.add(cur_episode / duration)
self.collect_step += cur_step
self.collect_episode += cur_episode
self.collect_time += duration
if isinstance(n_episode, list):
n_episode = np.sum(n_episode)
else:
n_episode = max(cur_episode, 1)
reward_sum /= n_episode
if np.asanyarray(reward_sum).size > 1: # non-scalar reward_sum
reward_sum = self._rew_metric(reward_sum)
return {
'n/ep': cur_episode,
'n/st': cur_step,
'v/st': self.step_speed.get(),
'v/ep': self.episode_speed.get(),
'rew': reward_sum,
'len': length_sum / n_episode,
}
[docs] def sample(self, batch_size: int) -> Batch:
"""Sample a data batch from the internal replay buffer. It will call
:meth:`~tianshou.policy.BasePolicy.process_fn` before returning
the final batch data.
:param int batch_size: ``0`` means it will extract all the data from
the buffer, otherwise it will extract the data with the given
batch_size.
"""
batch_data, indice = self.buffer.sample(batch_size)
batch_data = self.process_fn(batch_data, self.buffer, indice)
return batch_data