from typing import Any, Dict, Optional, Type, TypeVar, Union
import torch as th
from gym import spaces
from torch.nn import functional as F
from stable_baselines3.common.on_policy_algorithm import OnPolicyAlgorithm
from stable_baselines3.common.policies import ActorCriticCnnPolicy, ActorCriticPolicy, BasePolicy, MultiInputActorCriticPolicy
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
from stable_baselines3.common.utils import explained_variance
SelfA2C = TypeVar("SelfA2C", bound="A2C")
[docs]class A2C(OnPolicyAlgorithm):
"""
Advantage Actor Critic (A2C)
Paper: https://arxiv.org/abs/1602.01783
Code: This implementation borrows code from https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail and
and Stable Baselines (https://github.com/hill-a/stable-baselines)
Introduction to A2C: https://hackernoon.com/intuitive-rl-intro-to-advantage-actor-critic-a2c-4ff545978752
: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 gamma: Discount factor
:param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator.
Equivalent to classic advantage when set to 1.
: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 rms_prop_eps: RMSProp epsilon. It stabilizes square root computation in denominator
of RMSProp update
:param use_rms_prop: Whether to use RMSprop (default) or Adam as optimizer
:param use_sde: Whether to use generalized State Dependent Exploration (gSDE)
instead of action noise exploration (default: False)
:param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
Default: -1 (only sample at the beginning of the rollout)
:param normalize_advantage: Whether to normalize or not the advantage
: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: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for
debug messages
: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: Dict[str, Type[BasePolicy]] = {
"MlpPolicy": ActorCriticPolicy,
"CnnPolicy": ActorCriticCnnPolicy,
"MultiInputPolicy": MultiInputActorCriticPolicy,
}
def __init__(
self,
policy: Union[str, Type[ActorCriticPolicy]],
env: Union[GymEnv, str],
learning_rate: Union[float, Schedule] = 7e-4,
n_steps: int = 5,
gamma: float = 0.99,
gae_lambda: float = 1.0,
ent_coef: float = 0.0,
vf_coef: float = 0.5,
max_grad_norm: float = 0.5,
rms_prop_eps: float = 1e-5,
use_rms_prop: bool = True,
use_sde: bool = False,
sde_sample_freq: int = -1,
normalize_advantage: bool = False,
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,
device=device,
seed=seed,
_init_setup_model=False,
supported_action_spaces=(
spaces.Box,
spaces.Discrete,
spaces.MultiDiscrete,
spaces.MultiBinary,
),
)
self.normalize_advantage = normalize_advantage
# Update optimizer inside the policy if we want to use RMSProp
# (original implementation) rather than Adam
if use_rms_prop and "optimizer_class" not in self.policy_kwargs:
self.policy_kwargs["optimizer_class"] = th.optim.RMSprop
self.policy_kwargs["optimizer_kwargs"] = dict(alpha=0.99, eps=rms_prop_eps, weight_decay=0)
if _init_setup_model:
self._setup_model()
[docs] def train(self) -> None:
"""
Update policy using the currently gathered
rollout buffer (one gradient step over whole data).
"""
# 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)
# This will only loop once (get all data in one go)
for rollout_data in self.rollout_buffer.get(batch_size=None):
actions = rollout_data.actions
if isinstance(self.action_space, spaces.Discrete):
# Convert discrete action from float to long
actions = actions.long().flatten()
values, log_prob, entropy = self.policy.evaluate_actions(rollout_data.observations, actions)
values = values.flatten()
# Normalize advantage (not present in the original implementation)
advantages = rollout_data.advantages
if self.normalize_advantage:
advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
# Policy gradient loss
policy_loss = -(advantages * log_prob).mean()
# Value loss using the TD(gae_lambda) target
value_loss = F.mse_loss(rollout_data.returns, values)
# Entropy loss favor exploration
if entropy is None:
# Approximate entropy when no analytical form
entropy_loss = -th.mean(-log_prob)
else:
entropy_loss = -th.mean(entropy)
loss = policy_loss + self.ent_coef * entropy_loss + self.vf_coef * value_loss
# 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()
explained_var = explained_variance(self.rollout_buffer.values.flatten(), self.rollout_buffer.returns.flatten())
self._n_updates += 1
self.logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
self.logger.record("train/explained_variance", explained_var)
self.logger.record("train/entropy_loss", entropy_loss.item())
self.logger.record("train/policy_loss", policy_loss.item())
self.logger.record("train/value_loss", value_loss.item())
if hasattr(self.policy, "log_std"):
self.logger.record("train/std", th.exp(self.policy.log_std).mean().item())
[docs] def learn(
self: SelfA2C,
total_timesteps: int,
callback: MaybeCallback = None,
log_interval: int = 100,
tb_log_name: str = "A2C",
reset_num_timesteps: bool = True,
progress_bar: bool = False,
) -> SelfA2C:
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,
)