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