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import numpy as np
class Driver:
def __init__(self, envs, **kwargs):
self._envs = envs
self._kwargs = kwargs
self._on_steps = []
self._on_resets = []
self._on_episodes = []
self._act_spaces = [env.act_space for env in envs]
self.reset()
def on_step(self, callback):
self._on_steps.append(callback)
def on_reset(self, callback):
self._on_resets.append(callback)
def on_episode(self, callback):
self._on_episodes.append(callback)
def reset(self):
self._obs = [None] * len(self._envs)
self._eps = [None] * len(self._envs)
self._state = None
def __call__(self, policy, steps=0, episodes=0):
step, episode = 0, 0
while step < steps or episode < episodes:
obs = {
i: self._envs[i].reset()
for i, ob in enumerate(self._obs) if ob is None or ob['is_last']}
for i, ob in obs.items():
self._obs[i] = ob() if callable(ob) else ob
act = {k: np.zeros(v.shape) for k, v in self._act_spaces[i].items()}
tran = {k: self._convert(v) for k, v in {**ob, **act}.items()}
[fn(tran, worker=i, **self._kwargs) for fn in self._on_resets]
self._eps[i] = [tran]
obs = {k: np.stack([o[k] for o in self._obs]) for k in self._obs[0]}
actions, self._state = policy(obs, self._state, **self._kwargs)
actions = [
{k: np.array(actions[k][i]) for k in actions}
for i in range(len(self._envs))]
assert len(actions) == len(self._envs)
obs = [e.step(a) for e, a in zip(self._envs, actions)]
obs = [ob() if callable(ob) else ob for ob in obs]
for i, (act, ob) in enumerate(zip(actions, obs)):
tran = {k: self._convert(v) for k, v in {**ob, **act}.items()}
[fn(tran, worker=i, **self._kwargs) for fn in self._on_steps]
self._eps[i].append(tran)
step += 1
if ob['is_last']:
ep = self._eps[i]
ep = {k: self._convert([t[k] for t in ep]) for k in ep[0]}
[fn(ep, **self._kwargs) for fn in self._on_episodes]
episode += 1
self._obs = obs
def _convert(self, value):
value = np.array(value)
if np.issubdtype(value.dtype, np.floating):
return value.astype(np.float32)
elif np.issubdtype(value.dtype, np.signedinteger):
return value.astype(np.int32)
elif np.issubdtype(value.dtype, np.uint8):
return value.astype(np.uint8)
return value
class MultiEnvDriver:
def __init__(self, envs, modes, **kwargs):
self._envs = envs
self._kwargs = kwargs
self._on_steps = []
self._on_resets = []
self._on_episodes = []
self._act_spaces = [env.act_space for env in envs]
self.reset()
self.modes = modes
def on_step(self, callback):
self._on_steps.append(callback)
def on_reset(self, callback):
self._on_resets.append(callback)
def on_episode(self, callback):
self._on_episodes.append(callback)
def reset(self):
self._obs = [None] * len(self._envs)
self._eps = [None] * len(self._envs)
self._state = None
def __call__(self, policy, steps=0, episodes=0):
step, episode = 0, 0
while step < steps or episode < episodes:
obs = {
i: self._envs[i].reset()
for i, ob in enumerate(self._obs) if ob is None or ob['is_last']}
for i, ob in obs.items():
self._obs[i] = ob() if callable(ob) else ob
act = {k: np.zeros(v.shape) for k, v in self._act_spaces[i].items()}
tran = {k: self._convert(v) for k, v in {**ob, **act}.items()}
[fn(tran, worker=i, **self._kwargs) for fn in self._on_resets]
self._eps[i] = [tran]
obs = {k: np.stack([o[k] for o in self._obs]) for k in self._obs[0]}
actions, self._state = policy(obs, self._state, **self._kwargs)
actions = [
{k: np.array(actions[k][i]) for k in actions}
for i in range(len(self._envs))]
assert len(actions) == len(self._envs)
obs = [e.step(a) for e, a in zip(self._envs, actions)]
obs = [ob() if callable(ob) else ob for ob in obs]
for i, (act, ob) in enumerate(zip(actions, obs)):
tran = {k: self._convert(v) for k, v in {**ob, **act}.items()}
[fn(tran, worker=i, **self._kwargs) for fn in self._on_steps]
self._eps[i].append(tran)
step += 1
if ob['is_last']:
ep = self._eps[i]
ep = {k: self._convert([t[k] for t in ep]) for k in ep[0]}
[fn(ep, self.modes[i], **self._kwargs) for fn in self._on_episodes]
episode += 1
self._obs = obs
def _convert(self, value):
value = np.array(value)
if np.issubdtype(value.dtype, np.floating):
return value.astype(np.float32)
elif np.issubdtype(value.dtype, np.signedinteger):
return value.astype(np.int32)
elif np.issubdtype(value.dtype, np.uint8):
return value.astype(np.uint8)
return value
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