mirror of
https://github.com/saymrwulf/stable-baselines3.git
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166 lines
8.5 KiB
Python
166 lines
8.5 KiB
Python
import torch as th
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import torch.nn.functional as F
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from gym import spaces
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from typing import Type, Union, Callable, Optional, Dict, Any
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from stable_baselines3.common import logger
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from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback
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from stable_baselines3.common.utils import explained_variance
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from stable_baselines3.ppo.policies import PPOPolicy
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from stable_baselines3.ppo.ppo import PPO
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class A2C(PPO):
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"""
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Advantage Actor Critic (A2C)
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Paper: https://arxiv.org/abs/1602.01783
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Code: This implementation borrows code from https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail and
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and Stable Baselines (https://github.com/hill-a/stable-baselines)
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Introduction to A2C: https://hackernoon.com/intuitive-rl-intro-to-advantage-actor-critic-a2c-4ff545978752
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:param policy: (PPOPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, ...)
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:param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
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:param learning_rate: (float or callable) The learning rate, it can be a function
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:param n_steps: (int) The number of steps to run for each environment per update
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(i.e. batch size is n_steps * n_env where n_env is number of environment copies running in parallel)
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:param gamma: (float) Discount factor
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:param gae_lambda: (float) Factor for trade-off of bias vs variance for Generalized Advantage Estimator
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Equivalent to classic advantage when set to 1.
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:param ent_coef: (float) Entropy coefficient for the loss calculation
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:param vf_coef: (float) Value function coefficient for the loss calculation
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:param max_grad_norm: (float) The maximum value for the gradient clipping
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:param rms_prop_eps: (float) RMSProp epsilon. It stabilizes square root computation in denominator
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of RMSProp update
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:param use_rms_prop: (bool) Whether to use RMSprop (default) or Adam as optimizer
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:param use_sde: (bool) Whether to use State Dependent Exploration (SDE)
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instead of action noise exploration (default: False)
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:param sde_sample_freq: (int) Sample a new noise matrix every n steps when using SDE
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Default: -1 (only sample at the beginning of the rollout)
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:param normalize_advantage: (bool) Whether to normalize or not the advantage
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:param tensorboard_log: (str) the log location for tensorboard (if None, no logging)
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:param create_eval_env: (bool) Whether to create a second environment that will be
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used for evaluating the agent periodically. (Only available when passing string for the environment)
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:param policy_kwargs: (dict) additional arguments to be passed to the policy on creation
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:param verbose: (int) the verbosity level: 0 no output, 1 info, 2 debug
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:param seed: (int) Seed for the pseudo random generators
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:param device: (str or th.device) Device (cpu, cuda, ...) on which the code should be run.
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Setting it to auto, the code will be run on the GPU if possible.
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:param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance
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"""
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def __init__(self, policy: Union[str, Type[PPOPolicy]],
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env: Union[GymEnv, str],
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learning_rate: Union[float, Callable] = 7e-4,
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n_steps: int = 5,
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gamma: float = 0.99,
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gae_lambda: float = 1.0,
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ent_coef: float = 0.0,
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vf_coef: float = 0.5,
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max_grad_norm: float = 0.5,
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rms_prop_eps: float = 1e-5,
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use_rms_prop: bool = True,
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use_sde: bool = False,
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sde_sample_freq: int = -1,
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normalize_advantage: bool = False,
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tensorboard_log: Optional[str] = None,
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create_eval_env: bool = False,
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policy_kwargs: Optional[Dict[str, Any]] = None,
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verbose: int = 0,
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seed: Optional[int] = None,
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device: Union[th.device, str] = 'auto',
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_init_setup_model: bool = True):
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super(A2C, self).__init__(policy, env, learning_rate=learning_rate,
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n_steps=n_steps, batch_size=None, n_epochs=1,
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gamma=gamma, gae_lambda=gae_lambda, ent_coef=ent_coef,
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vf_coef=vf_coef, max_grad_norm=max_grad_norm,
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use_sde=use_sde, sde_sample_freq=sde_sample_freq,
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tensorboard_log=tensorboard_log, policy_kwargs=policy_kwargs,
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verbose=verbose, device=device, create_eval_env=create_eval_env,
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seed=seed, _init_setup_model=False)
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self.normalize_advantage = normalize_advantage
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# Override PPO optimizer to match original implementation
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if use_rms_prop and 'optimizer_class' not in self.policy_kwargs:
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self.policy_kwargs['optimizer_class'] = th.optim.RMSprop
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self.policy_kwargs['optimizer_kwargs'] = dict(alpha=0.99, eps=rms_prop_eps,
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weight_decay=0)
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if _init_setup_model:
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self._setup_model()
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def train(self, gradient_steps: int, batch_size: Optional[int] = None) -> None:
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# Update optimizer learning rate
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self._update_learning_rate(self.policy.optimizer)
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# A2C with gradient_steps > 1 does not make sense
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assert gradient_steps == 1, "A2C does not support multiple gradient steps"
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# We do not use minibatches for A2C
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assert batch_size is None, "A2C does not support minibatch"
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for rollout_data in self.rollout_buffer.get(batch_size=None):
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actions = rollout_data.actions
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if isinstance(self.action_space, spaces.Discrete):
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# Convert discrete action from float to long
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actions = actions.long().flatten()
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# TODO: avoid second computation of everything because of the gradient
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values, log_prob, entropy = self.policy.evaluate_actions(rollout_data.observations, actions)
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values = values.flatten()
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# Normalize advantage (not present in the original implementation)
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advantages = rollout_data.advantages
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if self.normalize_advantage:
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advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
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# Policy gradient loss
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policy_loss = -(advantages * log_prob).mean()
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# Value loss using the TD(gae_lambda) target
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value_loss = F.mse_loss(rollout_data.returns, values)
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# Entropy loss favor exploration
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if entropy is None:
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# Approximate entropy when no analytical form
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entropy_loss = -log_prob.mean()
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else:
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entropy_loss = -th.mean(entropy)
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loss = policy_loss + self.ent_coef * entropy_loss + self.vf_coef * value_loss
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# Optimization step
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self.policy.optimizer.zero_grad()
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loss.backward()
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# Clip grad norm
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th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
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self.policy.optimizer.step()
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explained_var = explained_variance(self.rollout_buffer.returns.flatten(),
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self.rollout_buffer.values.flatten())
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self._n_updates += 1
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logger.logkv("n_updates", self._n_updates)
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logger.logkv("explained_variance", explained_var)
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logger.logkv("entropy_loss", entropy_loss.item())
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logger.logkv("policy_loss", policy_loss.item())
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logger.logkv("value_loss", value_loss.item())
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if hasattr(self.policy, 'log_std'):
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logger.logkv("std", th.exp(self.policy.log_std).mean().item())
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def learn(self,
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total_timesteps: int,
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callback: MaybeCallback = None,
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log_interval: int = 100,
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eval_env: Optional[GymEnv] = None,
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eval_freq: int = -1,
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n_eval_episodes: int = 5,
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tb_log_name: str = "A2C",
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eval_log_path: Optional[str] = None,
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reset_num_timesteps: bool = True) -> 'A2C':
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return super(A2C, self).learn(total_timesteps=total_timesteps, callback=callback, log_interval=log_interval,
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eval_env=eval_env, eval_freq=eval_freq, n_eval_episodes=n_eval_episodes,
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tb_log_name=tb_log_name, eval_log_path=eval_log_path,
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reset_num_timesteps=reset_num_timesteps)
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