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