09692c84fe
Change the behavior of to_numpy and to_torch: from now on, dict is automatically converted to Batch and list is automatically converted to np.ndarray (if an error occurs, raise the exception instead of converting each element in the list).
216 lines
7.4 KiB
Python
216 lines
7.4 KiB
Python
import gym
|
|
import time
|
|
import ctypes
|
|
import numpy as np
|
|
from collections import OrderedDict
|
|
from multiprocessing.context import Process
|
|
from multiprocessing import Array, Pipe, connection
|
|
from typing import Any, List, Tuple, Union, Callable, Optional
|
|
|
|
from tianshou.env.worker import EnvWorker
|
|
from tianshou.env.utils import CloudpickleWrapper
|
|
|
|
|
|
_NP_TO_CT = {
|
|
np.bool_: ctypes.c_bool,
|
|
np.uint8: ctypes.c_uint8,
|
|
np.uint16: ctypes.c_uint16,
|
|
np.uint32: ctypes.c_uint32,
|
|
np.uint64: ctypes.c_uint64,
|
|
np.int8: ctypes.c_int8,
|
|
np.int16: ctypes.c_int16,
|
|
np.int32: ctypes.c_int32,
|
|
np.int64: ctypes.c_int64,
|
|
np.float32: ctypes.c_float,
|
|
np.float64: ctypes.c_double,
|
|
}
|
|
|
|
|
|
class ShArray:
|
|
"""Wrapper of multiprocessing Array."""
|
|
|
|
def __init__(self, dtype: np.generic, shape: Tuple[int]) -> None:
|
|
self.arr = Array(_NP_TO_CT[dtype.type], int(np.prod(shape))) # type: ignore
|
|
self.dtype = dtype
|
|
self.shape = shape
|
|
|
|
def save(self, ndarray: np.ndarray) -> None:
|
|
assert isinstance(ndarray, np.ndarray)
|
|
dst = self.arr.get_obj()
|
|
dst_np = np.frombuffer(dst, dtype=self.dtype).reshape(self.shape)
|
|
np.copyto(dst_np, ndarray)
|
|
|
|
def get(self) -> np.ndarray:
|
|
obj = self.arr.get_obj()
|
|
return np.frombuffer(obj, dtype=self.dtype).reshape(self.shape)
|
|
|
|
|
|
def _setup_buf(space: gym.Space) -> Union[dict, tuple, ShArray]:
|
|
if isinstance(space, gym.spaces.Dict):
|
|
assert isinstance(space.spaces, OrderedDict)
|
|
return {k: _setup_buf(v) for k, v in space.spaces.items()}
|
|
elif isinstance(space, gym.spaces.Tuple):
|
|
assert isinstance(space.spaces, tuple)
|
|
return tuple([_setup_buf(t) for t in space.spaces])
|
|
else:
|
|
return ShArray(space.dtype, space.shape)
|
|
|
|
|
|
def _worker(
|
|
parent: connection.Connection,
|
|
p: connection.Connection,
|
|
env_fn_wrapper: CloudpickleWrapper,
|
|
obs_bufs: Optional[Union[dict, tuple, ShArray]] = None,
|
|
) -> None:
|
|
def _encode_obs(
|
|
obs: Union[dict, tuple, np.ndarray], buffer: Union[dict, tuple, ShArray]
|
|
) -> None:
|
|
if isinstance(obs, np.ndarray) and isinstance(buffer, ShArray):
|
|
buffer.save(obs)
|
|
elif isinstance(obs, tuple) and isinstance(buffer, tuple):
|
|
for o, b in zip(obs, buffer):
|
|
_encode_obs(o, b)
|
|
elif isinstance(obs, dict) and isinstance(buffer, dict):
|
|
for k in obs.keys():
|
|
_encode_obs(obs[k], buffer[k])
|
|
return None
|
|
|
|
parent.close()
|
|
env = env_fn_wrapper.data()
|
|
try:
|
|
while True:
|
|
try:
|
|
cmd, data = p.recv()
|
|
except EOFError: # the pipe has been closed
|
|
p.close()
|
|
break
|
|
if cmd == "step":
|
|
obs, reward, done, info = env.step(data)
|
|
if obs_bufs is not None:
|
|
_encode_obs(obs, obs_bufs)
|
|
obs = None
|
|
p.send((obs, reward, done, info))
|
|
elif cmd == "reset":
|
|
obs = env.reset()
|
|
if obs_bufs is not None:
|
|
_encode_obs(obs, obs_bufs)
|
|
obs = None
|
|
p.send(obs)
|
|
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)
|
|
elif cmd == "getattr":
|
|
p.send(getattr(env, data) if hasattr(env, data) else None)
|
|
else:
|
|
p.close()
|
|
raise NotImplementedError
|
|
except KeyboardInterrupt:
|
|
p.close()
|
|
|
|
|
|
class SubprocEnvWorker(EnvWorker):
|
|
"""Subprocess worker used in SubprocVectorEnv and ShmemVectorEnv."""
|
|
|
|
def __init__(
|
|
self, env_fn: Callable[[], gym.Env], share_memory: bool = False
|
|
) -> None:
|
|
self.parent_remote, self.child_remote = Pipe()
|
|
self.share_memory = share_memory
|
|
self.buffer: Optional[Union[dict, tuple, ShArray]] = None
|
|
if self.share_memory:
|
|
dummy = env_fn()
|
|
obs_space = dummy.observation_space
|
|
dummy.close()
|
|
del dummy
|
|
self.buffer = _setup_buf(obs_space)
|
|
args = (
|
|
self.parent_remote,
|
|
self.child_remote,
|
|
CloudpickleWrapper(env_fn),
|
|
self.buffer,
|
|
)
|
|
self.process = Process(target=_worker, args=args, daemon=True)
|
|
self.process.start()
|
|
self.child_remote.close()
|
|
super().__init__(env_fn)
|
|
|
|
def __getattr__(self, key: str) -> Any:
|
|
self.parent_remote.send(["getattr", key])
|
|
return self.parent_remote.recv()
|
|
|
|
def _decode_obs(self) -> Union[dict, tuple, np.ndarray]:
|
|
def decode_obs(
|
|
buffer: Optional[Union[dict, tuple, ShArray]]
|
|
) -> Union[dict, tuple, np.ndarray]:
|
|
if isinstance(buffer, ShArray):
|
|
return buffer.get()
|
|
elif isinstance(buffer, tuple):
|
|
return tuple([decode_obs(b) for b in buffer])
|
|
elif isinstance(buffer, dict):
|
|
return {k: decode_obs(v) for k, v in buffer.items()}
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
return decode_obs(self.buffer)
|
|
|
|
def reset(self) -> Any:
|
|
self.parent_remote.send(["reset", None])
|
|
obs = self.parent_remote.recv()
|
|
if self.share_memory:
|
|
obs = self._decode_obs()
|
|
return obs
|
|
|
|
@staticmethod
|
|
def wait( # type: ignore
|
|
workers: List["SubprocEnvWorker"],
|
|
wait_num: int,
|
|
timeout: Optional[float] = None,
|
|
) -> List["SubprocEnvWorker"]:
|
|
remain_conns = conns = [x.parent_remote for x in workers]
|
|
ready_conns: List[connection.Connection] = []
|
|
remain_time, t1 = timeout, time.time()
|
|
while len(remain_conns) > 0 and len(ready_conns) < wait_num:
|
|
if timeout:
|
|
remain_time = timeout - (time.time() - t1)
|
|
if remain_time <= 0:
|
|
break
|
|
# connection.wait hangs if the list is empty
|
|
new_ready_conns = connection.wait(remain_conns, timeout=remain_time)
|
|
ready_conns.extend(new_ready_conns) # type: ignore
|
|
remain_conns = [conn for conn in remain_conns if conn not in ready_conns]
|
|
return [workers[conns.index(con)] for con in ready_conns]
|
|
|
|
def send_action(self, action: np.ndarray) -> None:
|
|
self.parent_remote.send(["step", action])
|
|
|
|
def get_result(self) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
|
|
obs, rew, done, info = self.parent_remote.recv()
|
|
if self.share_memory:
|
|
obs = self._decode_obs()
|
|
return obs, rew, done, info
|
|
|
|
def seed(self, seed: Optional[int] = None) -> Optional[List[int]]:
|
|
super().seed(seed)
|
|
self.parent_remote.send(["seed", seed])
|
|
return self.parent_remote.recv()
|
|
|
|
def render(self, **kwargs: Any) -> Any:
|
|
self.parent_remote.send(["render", kwargs])
|
|
return self.parent_remote.recv()
|
|
|
|
def close_env(self) -> None:
|
|
try:
|
|
self.parent_remote.send(["close", None])
|
|
# mp may be deleted so it may raise AttributeError
|
|
self.parent_remote.recv()
|
|
self.process.join()
|
|
except (BrokenPipeError, EOFError, AttributeError):
|
|
pass
|
|
# ensure the subproc is terminated
|
|
self.process.terminate()
|