103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
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from typing import Any, Dict, Optional, Type, Union, NamedTuple
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import numpy as np
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import torch as th
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from gym import spaces
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from stable_baselines3.common.buffers import RolloutBuffer
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from stable_baselines3.common.vec_env import VecNormalize
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class GaussianRolloutBufferSamples(NamedTuple):
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observations: th.Tensor
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actions: th.Tensor
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old_values: th.Tensor
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old_log_prob: th.Tensor
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advantages: th.Tensor
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returns: th.Tensor
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means: th.Tensor
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stds: th.Tensor
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class GaussianRolloutBuffer(RolloutBuffer):
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def __init__(
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self,
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buffer_size: int,
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observation_space: spaces.Space,
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action_space: spaces.Space,
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device: Union[th.device, str] = "cpu",
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gae_lambda: float = 1,
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gamma: float = 0.99,
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n_envs: int = 1,
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):
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super().__init__(buffer_size, observation_space, action_space,
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device, n_envs=n_envs, gae_lambda=gae_lambda, gamma=gamma)
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self.means, self.stds = None, None
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def reset(self) -> None:
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self.means = np.zeros(
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(self.buffer_size, self.n_envs) + self.action_space.shape, dtype=np.float32)
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self.stds = np.zeros(
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# (self.buffer_size, self.n_envs) + self.action_space.shape + self.action_space.shape, dtype=np.float32)
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(self.buffer_size, self.n_envs) + self.action_space.shape, dtype=np.float32)
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super().reset()
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def add(
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self,
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obs: np.ndarray,
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action: np.ndarray,
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reward: np.ndarray,
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episode_start: np.ndarray,
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value: th.Tensor,
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log_prob: th.Tensor,
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mean: th.Tensor,
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std: th.Tensor,
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) -> None:
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"""
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:param obs: Observation
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:param action: Action
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:param reward:
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:param episode_start: Start of episode signal.
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:param value: estimated value of the current state
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following the current policy.
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:param log_prob: log probability of the action
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following the current policy.
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:param mean: Foo
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:param std: Bar
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"""
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if len(log_prob.shape) == 0:
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# Reshape 0-d tensor to avoid error
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log_prob = log_prob.reshape(-1, 1)
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# Reshape needed when using multiple envs with discrete observations
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# as numpy cannot broadcast (n_discrete,) to (n_discrete, 1)
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if isinstance(self.observation_space, spaces.Discrete):
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obs = obs.reshape((self.n_envs,) + self.obs_shape)
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self.observations[self.pos] = np.array(obs).copy()
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self.actions[self.pos] = np.array(action).copy()
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self.rewards[self.pos] = np.array(reward).copy()
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self.episode_starts[self.pos] = np.array(episode_start).copy()
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self.values[self.pos] = value.clone().cpu().numpy().flatten()
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self.log_probs[self.pos] = log_prob.clone().cpu().numpy()
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self.means[self.pos] = mean.clone().cpu().numpy()
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self.stds[self.pos] = std.clone().cpu().numpy()
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self.pos += 1
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if self.pos == self.buffer_size:
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self.full = True
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def _get_samples(self, batch_inds: np.ndarray, env: Optional[VecNormalize] = None) -> GaussianRolloutBufferSamples:
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data = (
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self.observations[batch_inds],
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self.actions[batch_inds],
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self.values[batch_inds].flatten(),
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self.log_probs[batch_inds].flatten(),
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self.advantages[batch_inds].flatten(),
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self.returns[batch_inds].flatten(),
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self.means[batch_inds].reshape((len(batch_inds), -1)),
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self.stds[batch_inds].reshape((len(batch_inds), -1)),
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)
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return GaussianRolloutBufferSamples(*tuple(map(self.to_torch, data)))
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