from typing import Any, Dict, Optional, Type, Union, NamedTuple, Generator, List
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
from stable_baselines3.common.vec_env import VecEnv, DummyVecEnv
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3.common.utils import obs_as_tensor
from ..misc.distTools import get_mean_and_chol
from ..distributions.distributions import Strength, UniversalGaussianDistribution
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
chols: 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_dim), dtype=np.float32)
self.chols = 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,
chol: 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:
:param chol:
"""
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] = np.array(mean).copy()
self.chols[self.pos] = np.array(chol).copy()
self.pos += 1
if self.pos == self.buffer_size:
self.full = True
def get(self, batch_size: Optional[int] = None) -> Generator[GaussianRolloutBufferSamples, None, None]:
assert self.full, ""
indices = np.random.permutation(self.buffer_size * self.n_envs)
# Prepare the data
if not self.generator_ready:
_tensor_names = [
"observations",
"actions",
"values",
"log_probs",
"advantages",
"returns",
"means",
"chols"
]
for tensor in _tensor_names:
self.__dict__[tensor] = self.swap_and_flatten(
self.__dict__[tensor])
self.generator_ready = True
# Return everything, don't create minibatches
if batch_size is None:
batch_size = self.buffer_size * self.n_envs
start_idx = 0
while start_idx < self.buffer_size * self.n_envs:
yield self._get_samples(indices[start_idx: start_idx + batch_size])
start_idx += batch_size
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.chols[batch_inds].reshape(
(len(batch_inds),) + self.cov_shape),
)
return GaussianRolloutBufferSamples(*tuple(map(self.to_torch, data)))
class GaussianRolloutCollectorAuxclass():
def _setup_model(self) -> None:
super()._setup_model()
cov_shape = self.action_space.shape
if isinstance(self.policy.action_dist, UniversalGaussianDistribution):
if self.policy.action_dist.cov_strength == Strength.FULL:
cov_shape = cov_shape + cov_shape
self.rollout_buffer = GaussianRolloutBuffer(
self.n_steps,
self.observation_space,
self.action_space,
device=self.device,
gamma=self.gamma,
gae_lambda=self.gae_lambda,
n_envs=self.n_envs,
cov_shape=cov_shape,
)
def collect_rollouts(
self,
env: VecEnv,
callback: BaseCallback,
rollout_buffer: RolloutBuffer,
n_rollout_steps: int,
) -> bool:
"""
Collect experiences using the current policy and fill a ``RolloutBuffer``.
The term rollout here refers to the model-free notion and should not
be used with the concept of rollout used in model-based RL or planning.
:param env: The training environment
:param callback: Callback that will be called at each step
(and at the beginning and end of the rollout)
:param rollout_buffer: Buffer to fill with rollouts
:param n_steps: Number of experiences to collect per environment
:return: True if function returned with at least `n_rollout_steps`
collected, False if callback terminated rollout prematurely.
"""
assert self._last_obs is not None, "No previous observation was provided"
# Switch to eval mode (this affects batch norm / dropout)
self.policy.set_training_mode(False)
n_steps = 0
rollout_buffer.reset()
# Sample new weights for the state dependent exploration
if self.use_sde:
self.policy.reset_noise(env.num_envs)
callback.on_rollout_start()
while n_steps < n_rollout_steps:
if self.use_sde and self.sde_sample_freq > 0 and n_steps % self.sde_sample_freq == 0:
# Sample a new noise matrix
self.policy.reset_noise(env.num_envs)
with th.no_grad():
# Convert to pytorch tensor or to TensorDict
obs_tensor = obs_as_tensor(self._last_obs, self.device)
if self.policy['use_pca']:
actions, values, log_probs = self.policy(obs_tensor, trajectory=self.get_past_trajectories())
else:
actions, values, log_probs = self.policy(obs_tensor)
dist = self.policy.get_distribution(obs_tensor).distribution
mean, chol = get_mean_and_chol(dist)
actions = actions.cpu().numpy()
mean = mean.cpu().numpy()
chol = chol.cpu().numpy()
# Rescale and perform action
clipped_actions = actions
# Clip the actions to avoid out of bound error
if isinstance(self.action_space, spaces.Box):
clipped_actions = np.clip(
actions, self.action_space.low, self.action_space.high)
new_obs, rewards, dones, infos = env.step(clipped_actions)
self.num_timesteps += env.num_envs
# Give access to local variables
callback.update_locals(locals())
if callback.on_step() is False:
return False
self._update_info_buffer(infos)
n_steps += 1
if isinstance(self.action_space, spaces.Discrete):
# Reshape in case of discrete action
actions = actions.reshape(-1, 1)
# Handle timeout by bootstraping with value function
# see GitHub issue #633
for idx, done in enumerate(dones):
if (
done
and infos[idx].get("terminal_observation") is not None
and infos[idx].get("TimeLimit.truncated", False)
):
terminal_obs = self.policy.obs_to_tensor(
infos[idx]["terminal_observation"])[0]
with th.no_grad():
terminal_value = self.policy.predict_values(terminal_obs)[
0]
rewards[idx] += self.gamma * terminal_value
rollout_buffer.add(self._last_obs, actions, rewards,
self._last_episode_starts, values, log_probs, mean, chol)
self._last_obs = new_obs
self._last_episode_starts = dones
with th.no_grad():
# Compute value for the last timestep
values = self.policy.predict_values(
obs_as_tensor(new_obs, self.device))
rollout_buffer.compute_returns_and_advantage(
last_values=values, dones=dones)
callback.on_rollout_end()
return True
def get_past_trajectories(self):
# TODO: Respect Episode Boundaries
return self.actions