import torch as th import torch.nn.functional as F from typing import List, Tuple, Type, Union, Callable, Optional, Dict, Any from torchy_baselines.common import logger from torchy_baselines.common.base_class import OffPolicyRLModel from torchy_baselines.common.noise import ActionNoise from torchy_baselines.common.type_aliases import GymEnv, MaybeCallback from torchy_baselines.td3.policies import TD3Policy class TD3(OffPolicyRLModel): """ Twin Delayed DDPG (TD3) Addressing Function Approximation Error in Actor-Critic Methods. Original implementation: https://github.com/sfujim/TD3 Paper: https://arxiv.org/abs/1802.09477 Introduction to TD3: https://spinningup.openai.com/en/latest/algorithms/td3.html :param policy: (TD3Policy or str) The policy model to use (MlpPolicy, CnnPolicy, ...) :param env: (GymEnv or str) The environment to learn from (if registered in Gym, can be str) :param learning_rate: (float or callable) learning rate for adam optimizer, the same learning rate will be used for all networks (Q-Values, Actor and Value function) it can be a function of the current progress (from 1 to 0) :param buffer_size: (int) size of the replay buffer :param learning_starts: (int) how many steps of the model to collect transitions for before learning starts :param batch_size: (int) Minibatch size for each gradient update :param tau: (float) the soft update coefficient ("polyak update", between 0 and 1) :param gamma: (float) the discount factor :param train_freq: (int) Update the model every ``train_freq`` steps. :param gradient_steps: (int) How many gradient update after each step :param n_episodes_rollout: (int) Update the model every ``n_episodes_rollout`` episodes. Note that this cannot be used at the same time as ``train_freq`` :param action_noise: (ActionNoise) the action noise type (None by default), this can help for hard exploration problem. Cf common.noise for the different action noise type. :param policy_delay: (int) Policy and target networks will only be updated once every policy_delay steps per training steps. The Q values will be updated policy_delay more often (update every training step). :param target_policy_noise: (float) Standard deviation of Gaussian noise added to target policy (smoothing noise) :param target_noise_clip: (float) Limit for absolute value of target policy smoothing noise. :param use_sde: (bool) Whether to use State Dependent Exploration (SDE) instead of action noise exploration (default: False) :param sde_sample_freq: (int) Sample a new noise matrix every n steps when using SDE Default: -1 (only sample at the beginning of the rollout) :param sde_max_grad_norm: (float) :param sde_ent_coef: (float) :param sde_log_std_scheduler: (callable) :param use_sde_at_warmup: (bool) Whether to use SDE instead of uniform sampling during the warm up phase (before learning starts) :param create_eval_env: (bool) Whether to create a second environment that will be used for evaluating the agent periodically. (Only available when passing string for the environment) :param policy_kwargs: (dict) additional arguments to be passed to the policy on creation :param verbose: (int) the verbosity level: 0 no output, 1 info, 2 debug :param seed: (int) Seed for the pseudo random generators :param device: (str or th.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: (bool) Whether or not to build the network at the creation of the instance """ def __init__(self, policy: Union[str, Type[TD3Policy]], env: Union[GymEnv, str], learning_rate: Union[float, Callable] = 1e-3, buffer_size: int = int(1e6), learning_starts: int = 100, batch_size: int = 100, tau: float = 0.005, gamma: float = 0.99, train_freq: int = -1, gradient_steps: int = -1, n_episodes_rollout: int = 1, action_noise: Optional[ActionNoise] = None, policy_delay: int = 2, target_policy_noise: float = 0.2, target_noise_clip: float = 0.5, use_sde: bool = False, sde_sample_freq: int = -1, sde_max_grad_norm: float = 1, sde_ent_coef: float = 0.0, sde_log_std_scheduler: Optional[Callable] = None, use_sde_at_warmup: bool = False, tensorboard_log: Optional[str] = None, create_eval_env: bool = False, policy_kwargs: Dict[str, Any] = None, verbose: int = 0, seed: Optional[int] = None, device: Union[th.device, str] = 'auto', _init_setup_model: bool = True): super(TD3, self).__init__(policy, env, TD3Policy, learning_rate, buffer_size, learning_starts, batch_size, policy_kwargs, verbose, device, create_eval_env=create_eval_env, seed=seed, use_sde=use_sde, sde_sample_freq=sde_sample_freq, use_sde_at_warmup=use_sde_at_warmup) self.train_freq = train_freq self.gradient_steps = gradient_steps self.n_episodes_rollout = n_episodes_rollout self.tau = tau self.gamma = gamma self.action_noise = action_noise self.policy_delay = policy_delay self.target_noise_clip = target_noise_clip self.target_policy_noise = target_policy_noise # State Dependent Exploration self.sde_max_grad_norm = sde_max_grad_norm self.sde_ent_coef = sde_ent_coef self.sde_log_std_scheduler = sde_log_std_scheduler self.on_policy_exploration = True self.sde_vf = None if _init_setup_model: self._setup_model() def _setup_model(self) -> None: super(TD3, self)._setup_model() self._create_aliases() def _create_aliases(self) -> None: self.actor = self.policy.actor self.actor_target = self.policy.actor_target self.critic = self.policy.critic self.critic_target = self.policy.critic_target self.vf_net = self.policy.vf_net def train(self, gradient_steps: int, batch_size: int = 100, policy_delay: int = 2) -> None: # Update learning rate according to lr schedule self._update_learning_rate([self.actor.optimizer, self.critic.optimizer]) for gradient_step in range(gradient_steps): # Sample replay buffer replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env) with th.no_grad(): # Select action according to policy and add clipped noise noise = replay_data.actions.clone().data.normal_(0, self.target_policy_noise) noise = noise.clamp(-self.target_noise_clip, self.target_noise_clip) next_actions = (self.actor_target(replay_data.next_observations) + noise).clamp(-1, 1) # Compute the target Q value target_q1, target_q2 = self.critic_target(replay_data.next_observations, next_actions) target_q = th.min(target_q1, target_q2) target_q = replay_data.rewards + (1 - replay_data.dones) * self.gamma * target_q # Get current Q estimates current_q1, current_q2 = self.critic(replay_data.observations, replay_data.actions) # Compute critic loss critic_loss = F.mse_loss(current_q1, target_q) + F.mse_loss(current_q2, target_q) # Optimize the critic self.critic.optimizer.zero_grad() critic_loss.backward() self.critic.optimizer.step() # Delayed policy updates if gradient_step % policy_delay == 0: # Compute actor loss actor_loss = -self.critic.q1_forward(replay_data.observations, self.actor(replay_data.observations)).mean() # Optimize the actor self.actor.optimizer.zero_grad() actor_loss.backward() self.actor.optimizer.step() # Update the frozen target networks for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()): target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data) for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()): target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data) self._n_updates += gradient_steps logger.logkv("n_updates", self._n_updates) def train_sde(self) -> None: # Update optimizer learning rate # self._update_learning_rate(self.policy.optimizer) # Unpack obs, action, advantage, returns = [self.rollout_data[key] for key in ['observations', 'actions', 'advantage', 'returns']] log_prob, entropy = self.actor.evaluate_actions(obs, action) values = self.vf_net(obs).flatten() # Normalize advantage # if self.normalize_advantage: # advantage = (advantage - advantage.mean()) / (advantage.std() + 1e-8) # Value loss using the TD(gae_lambda) target value_loss = F.mse_loss(returns, values) # A2C loss policy_loss = -(advantage * log_prob).mean() # Entropy loss favor exploration if entropy is None: # Approximate entropy when no analytical form entropy_loss = -log_prob.mean() else: entropy_loss = -th.mean(entropy) vf_coef = 0.5 loss = policy_loss + self.sde_ent_coef * entropy_loss + vf_coef * value_loss # Optimization step self.actor.sde_optimizer.zero_grad() loss.backward() assert not th.isnan(log_prob).any(), log_prob assert not th.isnan(entropy).any() assert not th.isnan(self.actor.log_std.grad).any() assert not th.isnan(self.actor.log_std).any() # Clip grad norm th.nn.utils.clip_grad_norm_([self.actor.log_std], self.sde_max_grad_norm) self.actor.sde_optimizer.step() del self.rollout_data def learn(self, total_timesteps: int, callback: MaybeCallback = None, log_interval: int = 4, eval_env: Optional[GymEnv] = None, eval_freq: int = -1, n_eval_episodes: int = 5, tb_log_name: str = "TD3", eval_log_path: Optional[str] = None, reset_num_timesteps: bool = True) -> OffPolicyRLModel: callback = self._setup_learn(eval_env, callback, eval_freq, n_eval_episodes, eval_log_path, reset_num_timesteps) callback.on_training_start(locals(), globals()) while self.num_timesteps < total_timesteps: rollout = self.collect_rollouts(self.env, n_episodes=self.n_episodes_rollout, n_steps=self.train_freq, action_noise=self.action_noise, callback=callback, learning_starts=self.learning_starts, replay_buffer=self.replay_buffer, log_interval=log_interval) if rollout.continue_training is False: break self._update_current_progress(self.num_timesteps, total_timesteps) if self.num_timesteps > 0 and self.num_timesteps > self.learning_starts: if self.use_sde: if self.sde_log_std_scheduler is not None: # Call the scheduler value = self.sde_log_std_scheduler(self._current_progress) self.actor.log_std.data = th.ones_like(self.actor.log_std) * value else: # On-policy gradient self.train_sde() gradient_steps = self.gradient_steps if self.gradient_steps > 0 else rollout.episode_timesteps self.train(gradient_steps, batch_size=self.batch_size, policy_delay=self.policy_delay) callback.on_training_end() return self def excluded_save_params(self) -> List[str]: """ Returns the names of the parameters that should be excluded by default when saving the model. :return: (List[str]) List of parameters that should be excluded from save """ # Exclude aliases return super(TD3, self).excluded_save_params() + ["actor", "critic", "vf_net", "actor_target", "critic_target"] def get_torch_variables(self) -> Tuple[List[str], List[str]]: """ cf base class """ state_dicts = ["policy", "actor.optimizer", "critic.optimizer"] return state_dicts, []