from copy import deepcopy
from typing import Any, ClassVar, Dict, Optional, Type, TypeVar, Union
import numpy as np
import torch as th
from gymnasium import spaces
from stable_baselines3.common.buffers import RolloutBuffer
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3.common.on_policy_algorithm import OnPolicyAlgorithm
from stable_baselines3.common.policies import BasePolicy
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
from stable_baselines3.common.utils import explained_variance, get_schedule_fn, obs_as_tensor
from stable_baselines3.common.vec_env import VecEnv
from sb3_contrib.common.recurrent.buffers import RecurrentDictRolloutBuffer, RecurrentRolloutBuffer
from sb3_contrib.common.recurrent.policies import RecurrentActorCriticPolicy
from sb3_contrib.common.recurrent.type_aliases import RNNStates
from sb3_contrib.ppo_recurrent.policies import CnnLstmPolicy, MlpLstmPolicy, MultiInputLstmPolicy
SelfRecurrentPPO = TypeVar("SelfRecurrentPPO", bound="RecurrentPPO")
[docs]class RecurrentPPO(OnPolicyAlgorithm):
"""
Proximal Policy Optimization algorithm (PPO) (clip version)
with support for recurrent policies (LSTM).
Based on the original Stable Baselines 3 implementation.
Introduction to PPO: https://spinningup.openai.com/en/latest/algorithms/ppo.html
:param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
:param env: The environment to learn from (if registered in Gym, can be str)
:param learning_rate: The learning rate, it can be a function
of the current progress remaining (from 1 to 0)
:param n_steps: The number of steps to run for each environment per update
(i.e. batch size is n_steps * n_env where n_env is number of environment copies running in parallel)
:param batch_size: Minibatch size
:param n_epochs: Number of epoch when optimizing the surrogate loss
:param gamma: Discount factor
:param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator
:param clip_range: Clipping parameter, it can be a function of the current progress
remaining (from 1 to 0).
:param clip_range_vf: Clipping parameter for the value function,
it can be a function of the current progress remaining (from 1 to 0).
This is a parameter specific to the OpenAI implementation. If None is passed (default),
no clipping will be done on the value function.
IMPORTANT: this clipping depends on the reward scaling.
:param normalize_advantage: Whether to normalize or not the advantage
:param ent_coef: Entropy coefficient for the loss calculation
:param vf_coef: Value function coefficient for the loss calculation
:param max_grad_norm: The maximum value for the gradient clipping
:param target_kl: Limit the KL divergence between updates,
because the clipping is not enough to prevent large update
see issue #213 (cf https://github.com/hill-a/stable-baselines/issues/213)
By default, there is no limit on the kl div.
:param stats_window_size: Window size for the rollout logging, specifying the number of episodes to average
the reported success rate, mean episode length, and mean reward over
:param tensorboard_log: the log location for tensorboard (if None, no logging)
:param policy_kwargs: additional arguments to be passed to the policy on creation
:param verbose: the verbosity level: 0 no output, 1 info, 2 debug
:param seed: Seed for the pseudo random generators
:param device: Device (cpu, cuda, ...) on which the code should be run.
Setting it to auto, the code will be run on the GPU if possible.
:param _init_setup_model: Whether or not to build the network at the creation of the instance
"""
policy_aliases: ClassVar[Dict[str, Type[BasePolicy]]] = {
"MlpLstmPolicy": MlpLstmPolicy,
"CnnLstmPolicy": CnnLstmPolicy,
"MultiInputLstmPolicy": MultiInputLstmPolicy,
}
def __init__(
self,
policy: Union[str, Type[RecurrentActorCriticPolicy]],
env: Union[GymEnv, str],
learning_rate: Union[float, Schedule] = 3e-4,
n_steps: int = 128,
batch_size: Optional[int] = 128,
n_epochs: int = 10,
gamma: float = 0.99,
gae_lambda: float = 0.95,
clip_range: Union[float, Schedule] = 0.2,
clip_range_vf: Union[None, float, Schedule] = None,
normalize_advantage: bool = True,
ent_coef: float = 0.0,
vf_coef: float = 0.5,
max_grad_norm: float = 0.5,
use_sde: bool = False,
sde_sample_freq: int = -1,
target_kl: Optional[float] = None,
stats_window_size: int = 100,
tensorboard_log: Optional[str] = None,
policy_kwargs: Optional[Dict[str, Any]] = None,
verbose: int = 0,
seed: Optional[int] = None,
device: Union[th.device, str] = "auto",
_init_setup_model: bool = True,
):
super().__init__(
policy,
env,
learning_rate=learning_rate,
n_steps=n_steps,
gamma=gamma,
gae_lambda=gae_lambda,
ent_coef=ent_coef,
vf_coef=vf_coef,
max_grad_norm=max_grad_norm,
use_sde=use_sde,
sde_sample_freq=sde_sample_freq,
stats_window_size=stats_window_size,
tensorboard_log=tensorboard_log,
policy_kwargs=policy_kwargs,
verbose=verbose,
seed=seed,
device=device,
_init_setup_model=False,
supported_action_spaces=(
spaces.Box,
spaces.Discrete,
spaces.MultiDiscrete,
spaces.MultiBinary,
),
)
self.batch_size = batch_size
self.n_epochs = n_epochs
self.clip_range = clip_range
self.clip_range_vf = clip_range_vf
self.normalize_advantage = normalize_advantage
self.target_kl = target_kl
self._last_lstm_states = None
if _init_setup_model:
self._setup_model()
def _setup_model(self) -> None:
self._setup_lr_schedule()
self.set_random_seed(self.seed)
buffer_cls = RecurrentDictRolloutBuffer if isinstance(self.observation_space, spaces.Dict) else RecurrentRolloutBuffer
self.policy = self.policy_class(
self.observation_space,
self.action_space,
self.lr_schedule,
use_sde=self.use_sde,
**self.policy_kwargs,
)
self.policy = self.policy.to(self.device)
# We assume that LSTM for the actor and the critic
# have the same architecture
lstm = self.policy.lstm_actor
if not isinstance(self.policy, RecurrentActorCriticPolicy):
raise ValueError("Policy must subclass RecurrentActorCriticPolicy")
single_hidden_state_shape = (lstm.num_layers, self.n_envs, lstm.hidden_size)
# hidden and cell states for actor and critic
self._last_lstm_states = RNNStates(
(
th.zeros(single_hidden_state_shape, device=self.device),
th.zeros(single_hidden_state_shape, device=self.device),
),
(
th.zeros(single_hidden_state_shape, device=self.device),
th.zeros(single_hidden_state_shape, device=self.device),
),
)
hidden_state_buffer_shape = (self.n_steps, lstm.num_layers, self.n_envs, lstm.hidden_size)
self.rollout_buffer = buffer_cls(
self.n_steps,
self.observation_space,
self.action_space,
hidden_state_buffer_shape,
self.device,
gamma=self.gamma,
gae_lambda=self.gae_lambda,
n_envs=self.n_envs,
)
# Initialize schedules for policy/value clipping
self.clip_range = get_schedule_fn(self.clip_range)
if self.clip_range_vf is not None:
if isinstance(self.clip_range_vf, (float, int)):
assert self.clip_range_vf > 0, "`clip_range_vf` must be positive, pass `None` to deactivate vf clipping"
self.clip_range_vf = get_schedule_fn(self.clip_range_vf)
[docs] 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 isinstance(
rollout_buffer, (RecurrentRolloutBuffer, RecurrentDictRolloutBuffer)
), f"{rollout_buffer} doesn't support recurrent policy"
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()
lstm_states = deepcopy(self._last_lstm_states)
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)
episode_starts = th.tensor(self._last_episode_starts, dtype=th.float32, device=self.device)
actions, values, log_probs, lstm_states = self.policy.forward(obs_tensor, lstm_states, episode_starts)
actions = actions.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 not callback.on_step():
return False
self._update_info_buffer(infos, dones)
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_lstm_state = (
lstm_states.vf[0][:, idx : idx + 1, :].contiguous(),
lstm_states.vf[1][:, idx : idx + 1, :].contiguous(),
)
# terminal_lstm_state = None
episode_starts = th.tensor([False], dtype=th.float32, device=self.device)
terminal_value = self.policy.predict_values(terminal_obs, terminal_lstm_state, episode_starts)[0]
rewards[idx] += self.gamma * terminal_value
rollout_buffer.add(
self._last_obs,
actions,
rewards,
self._last_episode_starts,
values,
log_probs,
lstm_states=self._last_lstm_states,
)
self._last_obs = new_obs
self._last_episode_starts = dones
self._last_lstm_states = lstm_states
with th.no_grad():
# Compute value for the last timestep
episode_starts = th.tensor(dones, dtype=th.float32, device=self.device)
values = self.policy.predict_values(obs_as_tensor(new_obs, self.device), lstm_states.vf, episode_starts)
rollout_buffer.compute_returns_and_advantage(last_values=values, dones=dones)
callback.on_rollout_end()
return True
[docs] def train(self) -> None:
"""
Update policy using the currently gathered rollout buffer.
"""
# Switch to train mode (this affects batch norm / dropout)
self.policy.set_training_mode(True)
# Update optimizer learning rate
self._update_learning_rate(self.policy.optimizer)
# Compute current clip range
clip_range = self.clip_range(self._current_progress_remaining)
# Optional: clip range for the value function
if self.clip_range_vf is not None:
clip_range_vf = self.clip_range_vf(self._current_progress_remaining)
entropy_losses = []
pg_losses, value_losses = [], []
clip_fractions = []
continue_training = True
# train for n_epochs epochs
for epoch in range(self.n_epochs):
approx_kl_divs = []
# Do a complete pass on the rollout buffer
for rollout_data in self.rollout_buffer.get(self.batch_size):
actions = rollout_data.actions
if isinstance(self.action_space, spaces.Discrete):
# Convert discrete action from float to long
actions = rollout_data.actions.long().flatten()
# Convert mask from float to bool
mask = rollout_data.mask > 1e-8
# Re-sample the noise matrix because the log_std has changed
if self.use_sde:
self.policy.reset_noise(self.batch_size)
values, log_prob, entropy = self.policy.evaluate_actions(
rollout_data.observations,
actions,
rollout_data.lstm_states,
rollout_data.episode_starts,
)
values = values.flatten()
# Normalize advantage
advantages = rollout_data.advantages
if self.normalize_advantage:
advantages = (advantages - advantages[mask].mean()) / (advantages[mask].std() + 1e-8)
# ratio between old and new policy, should be one at the first iteration
ratio = th.exp(log_prob - rollout_data.old_log_prob)
# clipped surrogate loss
policy_loss_1 = advantages * ratio
policy_loss_2 = advantages * th.clamp(ratio, 1 - clip_range, 1 + clip_range)
policy_loss = -th.mean(th.min(policy_loss_1, policy_loss_2)[mask])
# Logging
pg_losses.append(policy_loss.item())
clip_fraction = th.mean((th.abs(ratio - 1) > clip_range).float()[mask]).item()
clip_fractions.append(clip_fraction)
if self.clip_range_vf is None:
# No clipping
values_pred = values
else:
# Clip the different between old and new value
# NOTE: this depends on the reward scaling
values_pred = rollout_data.old_values + th.clamp(
values - rollout_data.old_values, -clip_range_vf, clip_range_vf
)
# Value loss using the TD(gae_lambda) target
# Mask padded sequences
value_loss = th.mean(((rollout_data.returns - values_pred) ** 2)[mask])
value_losses.append(value_loss.item())
# Entropy loss favor exploration
if entropy is None:
# Approximate entropy when no analytical form
entropy_loss = -th.mean(-log_prob[mask])
else:
entropy_loss = -th.mean(entropy[mask])
entropy_losses.append(entropy_loss.item())
loss = policy_loss + self.ent_coef * entropy_loss + self.vf_coef * value_loss
# Calculate approximate form of reverse KL Divergence for early stopping
# see issue #417: https://github.com/DLR-RM/stable-baselines3/issues/417
# and discussion in PR #419: https://github.com/DLR-RM/stable-baselines3/pull/419
# and Schulman blog: http://joschu.net/blog/kl-approx.html
with th.no_grad():
log_ratio = log_prob - rollout_data.old_log_prob
approx_kl_div = th.mean(((th.exp(log_ratio) - 1) - log_ratio)[mask]).cpu().numpy()
approx_kl_divs.append(approx_kl_div)
if self.target_kl is not None and approx_kl_div > 1.5 * self.target_kl:
continue_training = False
if self.verbose >= 1:
print(f"Early stopping at step {epoch} due to reaching max kl: {approx_kl_div:.2f}")
break
# Optimization step
self.policy.optimizer.zero_grad()
loss.backward()
# Clip grad norm
th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
self.policy.optimizer.step()
if not continue_training:
break
self._n_updates += self.n_epochs
explained_var = explained_variance(self.rollout_buffer.values.flatten(), self.rollout_buffer.returns.flatten())
# Logs
self.logger.record("train/entropy_loss", np.mean(entropy_losses))
self.logger.record("train/policy_gradient_loss", np.mean(pg_losses))
self.logger.record("train/value_loss", np.mean(value_losses))
self.logger.record("train/approx_kl", np.mean(approx_kl_divs))
self.logger.record("train/clip_fraction", np.mean(clip_fractions))
self.logger.record("train/loss", loss.item())
self.logger.record("train/explained_variance", explained_var)
if hasattr(self.policy, "log_std"):
self.logger.record("train/std", th.exp(self.policy.log_std).mean().item())
self.logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
self.logger.record("train/clip_range", clip_range)
if self.clip_range_vf is not None:
self.logger.record("train/clip_range_vf", clip_range_vf)
[docs] def learn(
self: SelfRecurrentPPO,
total_timesteps: int,
callback: MaybeCallback = None,
log_interval: int = 1,
tb_log_name: str = "RecurrentPPO",
reset_num_timesteps: bool = True,
progress_bar: bool = False,
) -> SelfRecurrentPPO:
return super().learn(
total_timesteps=total_timesteps,
callback=callback,
log_interval=log_interval,
tb_log_name=tb_log_name,
reset_num_timesteps=reset_num_timesteps,
progress_bar=progress_bar,
)