import gym
import numpy as np
from abc import ABC, abstractmethod
from multiprocessing import Process, Pipe
from typing import List, Tuple, Union, Optional, Callable
try:
import ray
except ImportError:
pass
from tianshou.env.utils import CloudpickleWrapper
[docs]class BaseVectorEnv(ABC, gym.Env):
"""Base class for vectorized environments wrapper. Usage:
::
env_num = 8
envs = VectorEnv([lambda: gym.make(task) for _ in range(env_num)])
assert len(envs) == env_num
It accepts a list of environment generators. In other words, an environment
generator ``efn`` of a specific task means that ``efn()`` returns the
environment of the given task, for example, ``gym.make(task)``.
All of the VectorEnv must inherit :class:`~tianshou.env.BaseVectorEnv`.
Here are some other usages:
::
envs.seed(2) # which is equal to the next line
envs.seed([2, 3, 4, 5, 6, 7, 8, 9]) # set specific seed for each env
obs = envs.reset() # reset all environments
obs = envs.reset([0, 5, 7]) # reset 3 specific environments
obs, rew, done, info = envs.step([1] * 8) # step synchronously
envs.render() # render all environments
envs.close() # close all environments
"""
def __init__(self, env_fns: List[Callable[[], gym.Env]]) -> None:
self._env_fns = env_fns
self.env_num = len(env_fns)
[docs] def __len__(self) -> int:
"""Return len(self), which is the number of environments."""
return self.env_num
[docs] @abstractmethod
def reset(self, id: Optional[Union[int, List[int]]] = None):
"""Reset the state of all the environments and return initial
observations if id is ``None``, otherwise reset the specific
environments with given id, either an int or a list.
"""
pass
[docs] @abstractmethod
def step(self, action: np.ndarray
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Run one timestep of all the environments’ dynamics. When the end of
episode is reached, you are responsible for calling reset(id) to reset
this environment’s state.
Accept a batch of action and return a tuple (obs, rew, done, info).
:param numpy.ndarray action: a batch of action provided by the agent.
:return: A tuple including four items:
* ``obs`` a numpy.ndarray, the agent's observation of current \
environments
* ``rew`` a numpy.ndarray, the amount of rewards returned after \
previous actions
* ``done`` a numpy.ndarray, whether these episodes have ended, in \
which case further step() calls will return undefined results
* ``info`` a numpy.ndarray, contains auxiliary diagnostic \
information (helpful for debugging, and sometimes learning)
"""
pass
[docs] @abstractmethod
def seed(self, seed: Optional[Union[int, List[int]]] = None) -> None:
"""Set the seed for all environments.
Accept ``None``, an int (which will extend ``i`` to
``[i, i + 1, i + 2, ...]``) or a list.
:return: The list of seeds used in this env's random number generators.
The first value in the list should be the "main" seed, or the value
which a reproducer should pass to 'seed'.
"""
pass
[docs] @abstractmethod
def render(self, **kwargs) -> None:
"""Render all of the environments."""
pass
[docs] @abstractmethod
def close(self) -> None:
"""Close all of the environments.
Environments will automatically close() themselves when garbage
collected or when the program exits.
"""
pass
[docs]class VectorEnv(BaseVectorEnv):
"""Dummy vectorized environment wrapper, implemented in for-loop.
.. seealso::
Please refer to :class:`~tianshou.env.BaseVectorEnv` for more detailed
explanation.
"""
def __init__(self, env_fns: List[Callable[[], gym.Env]]) -> None:
super().__init__(env_fns)
self.envs = [_() for _ in env_fns]
[docs] def reset(self, id: Optional[Union[int, List[int]]] = None) -> None:
if id is None:
self._obs = np.stack([e.reset() for e in self.envs])
else:
if np.isscalar(id):
id = [id]
for i in id:
self._obs[i] = self.envs[i].reset()
return self._obs
[docs] def step(self, action: np.ndarray
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
assert len(action) == self.env_num
result = [e.step(a) for e, a in zip(self.envs, action)]
self._obs, self._rew, self._done, self._info = zip(*result)
self._obs = np.stack(self._obs)
self._rew = np.stack(self._rew)
self._done = np.stack(self._done)
self._info = np.stack(self._info)
return self._obs, self._rew, self._done, self._info
[docs] def seed(self, seed: Optional[Union[int, List[int]]] = None) -> None:
if np.isscalar(seed):
seed = [seed + _ for _ in range(self.env_num)]
elif seed is None:
seed = [seed] * self.env_num
result = []
for e, s in zip(self.envs, seed):
if hasattr(e, 'seed'):
result.append(e.seed(s))
return result
[docs] def render(self, **kwargs) -> None:
result = []
for e in self.envs:
if hasattr(e, 'render'):
result.append(e.render(**kwargs))
return result
[docs] def close(self) -> None:
return [e.close() for e in self.envs]
def worker(parent, p, env_fn_wrapper):
parent.close()
env = env_fn_wrapper.data()
try:
while True:
cmd, data = p.recv()
if cmd == 'step':
p.send(env.step(data))
elif cmd == 'reset':
p.send(env.reset())
elif cmd == 'close':
p.send(env.close())
p.close()
break
elif cmd == 'render':
p.send(env.render(**data) if hasattr(env, 'render') else None)
elif cmd == 'seed':
p.send(env.seed(data) if hasattr(env, 'seed') else None)
else:
p.close()
raise NotImplementedError
except KeyboardInterrupt:
p.close()
[docs]class SubprocVectorEnv(BaseVectorEnv):
"""Vectorized environment wrapper based on subprocess.
.. seealso::
Please refer to :class:`~tianshou.env.BaseVectorEnv` for more detailed
explanation.
"""
def __init__(self, env_fns: List[Callable[[], gym.Env]]) -> None:
super().__init__(env_fns)
self.closed = False
self.parent_remote, self.child_remote = \
zip(*[Pipe() for _ in range(self.env_num)])
self.processes = [
Process(target=worker, args=(
parent, child, CloudpickleWrapper(env_fn)), daemon=True)
for (parent, child, env_fn) in zip(
self.parent_remote, self.child_remote, env_fns)
]
for p in self.processes:
p.start()
for c in self.child_remote:
c.close()
[docs] def step(self, action: np.ndarray
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
assert len(action) == self.env_num
for p, a in zip(self.parent_remote, action):
p.send(['step', a])
result = [p.recv() for p in self.parent_remote]
self._obs, self._rew, self._done, self._info = zip(*result)
self._obs = np.stack(self._obs)
self._rew = np.stack(self._rew)
self._done = np.stack(self._done)
self._info = np.stack(self._info)
return self._obs, self._rew, self._done, self._info
[docs] def reset(self, id: Optional[Union[int, List[int]]] = None) -> None:
if id is None:
for p in self.parent_remote:
p.send(['reset', None])
self._obs = np.stack([p.recv() for p in self.parent_remote])
return self._obs
else:
if np.isscalar(id):
id = [id]
for i in id:
self.parent_remote[i].send(['reset', None])
for i in id:
self._obs[i] = self.parent_remote[i].recv()
return self._obs
[docs] def seed(self, seed: Optional[Union[int, List[int]]] = None) -> None:
if np.isscalar(seed):
seed = [seed + _ for _ in range(self.env_num)]
elif seed is None:
seed = [seed] * self.env_num
for p, s in zip(self.parent_remote, seed):
p.send(['seed', s])
return [p.recv() for p in self.parent_remote]
[docs] def render(self, **kwargs) -> None:
for p in self.parent_remote:
p.send(['render', kwargs])
return [p.recv() for p in self.parent_remote]
[docs] def close(self) -> None:
if self.closed:
return
for p in self.parent_remote:
p.send(['close', None])
result = [p.recv() for p in self.parent_remote]
self.closed = True
for p in self.processes:
p.join()
return result
[docs]class RayVectorEnv(BaseVectorEnv):
"""Vectorized environment wrapper based on
`ray <https://github.com/ray-project/ray>`_. However, according to our
test, it is about two times slower than
:class:`~tianshou.env.SubprocVectorEnv`.
.. seealso::
Please refer to :class:`~tianshou.env.BaseVectorEnv` for more detailed
explanation.
"""
def __init__(self, env_fns: List[Callable[[], gym.Env]]) -> None:
super().__init__(env_fns)
try:
if not ray.is_initialized():
ray.init()
except NameError:
raise ImportError(
'Please install ray to support RayVectorEnv: pip3 install ray')
self.envs = [
ray.remote(gym.Wrapper).options(num_cpus=0).remote(e())
for e in env_fns]
[docs] def step(self, action: np.ndarray
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
assert len(action) == self.env_num
result = ray.get([e.step.remote(a) for e, a in zip(self.envs, action)])
self._obs, self._rew, self._done, self._info = zip(*result)
self._obs = np.stack(self._obs)
self._rew = np.stack(self._rew)
self._done = np.stack(self._done)
self._info = np.stack(self._info)
return self._obs, self._rew, self._done, self._info
[docs] def reset(self, id: Optional[Union[int, List[int]]] = None) -> None:
if id is None:
result_obj = [e.reset.remote() for e in self.envs]
self._obs = np.stack(ray.get(result_obj))
else:
result_obj = []
if np.isscalar(id):
id = [id]
for i in id:
result_obj.append(self.envs[i].reset.remote())
for _, i in enumerate(id):
self._obs[i] = ray.get(result_obj[_])
return self._obs
[docs] def seed(self, seed: Optional[Union[int, List[int]]] = None) -> None:
if not hasattr(self.envs[0], 'seed'):
return
if np.isscalar(seed):
seed = [seed + _ for _ in range(self.env_num)]
elif seed is None:
seed = [seed] * self.env_num
return ray.get([e.seed.remote(s) for e, s in zip(self.envs, seed)])
[docs] def render(self, **kwargs) -> None:
if not hasattr(self.envs[0], 'render'):
return
return ray.get([e.render.remote(**kwargs) for e in self.envs])
[docs] def close(self) -> None:
return ray.get([e.close.remote() for e in self.envs])