import time import torch as th import torch.nn.functional as F import numpy as np from torchy_baselines.common.base_class import BaseRLModel from torchy_baselines.common.replay_buffer import ReplayBuffer from torchy_baselines.common.utils import set_random_seed from torchy_baselines.common.evaluation import evaluate_policy from torchy_baselines.td3.policies import TD3Policy class TD3(BaseRLModel): """ Implementation of Twin Delayed Deep Deterministic Policy Gradients (TD3) Paper: https://arxiv.org/abs/1802.09477 Code: https://github.com/sfujim/TD3 """ def __init__(self, policy, env, policy_kwargs=None, verbose=0, buffer_size=int(1e6), learning_rate=1e-3, seed=0, device='auto', action_noise_std=0.1, start_timesteps=100, policy_freq=2, batch_size=100, _init_setup_model=True): super(TD3, self).__init__(policy, env, TD3Policy, policy_kwargs, verbose, device) self.max_action = np.abs(self.action_space.high) self.action_noise_std = action_noise_std self.learning_rate = learning_rate self.buffer_size = buffer_size self.start_timesteps = start_timesteps self.seed = seed self.policy_freq = policy_freq self.batch_size = batch_size if _init_setup_model: self._setup_model() def _setup_model(self, seed=None): state_dim, action_dim = self.observation_space.shape[0], self.action_space.shape[0] set_random_seed(self.seed, using_cuda=self.device == th.device('cuda')) if self.env is not None: self.env.seed(self.seed) self.replay_buffer = ReplayBuffer(self.buffer_size, state_dim, action_dim, self.device) self.policy = self.policy(self.observation_space, self.action_space, self.learning_rate, device=self.device, **self.policy_kwargs) self._create_aliases() def _create_aliases(self): self.actor = self.policy.actor self.actor_target = self.policy.actor_target self.critic = self.policy.critic self.critic_target = self.policy.critic_target def select_action(self, observation): # Normally not needed observation = np.array(observation) with th.no_grad(): observation = th.FloatTensor(observation.reshape(1, -1)).to(self.device) return self.actor(observation).cpu().data.numpy().flatten() def predict(self, observation, state=None, mask=None, deterministic=True): """ Get the model's action from an observation :param observation: (np.ndarray) the input observation :param state: (np.ndarray) The last states (can be None, used in recurrent policies) :param mask: (np.ndarray) The last masks (can be None, used in recurrent policies) :param deterministic: (bool) Whether or not to return deterministic actions. :return: (np.ndarray, np.ndarray) the model's action and the next state (used in recurrent policies) """ return self.max_action * self.select_action(observation) def train_critic(self, n_iterations=1, batch_size=100, discount=0.99, policy_noise=0.2, noise_clip=0.5, replay_data=None): for it in range(n_iterations): # Sample replay buffer if replay_data is None: state, action, next_state, done, reward = self.replay_buffer.sample(batch_size) else: state, action, next_state, done, reward = replay_data # Select action according to policy and add clipped noise noise = action.clone().data.normal_(0, policy_noise) noise = noise.clamp(-noise_clip, noise_clip) next_action = (self.actor_target(next_state) + noise).clamp(-1, 1) # Compute the target Q value target_q1, target_q2 = self.critic_target(next_state, next_action) target_q = th.min(target_q1, target_q2) target_q = reward + ((1 - done) * discount * target_q).detach() # Get current Q estimates current_q1, current_q2 = self.critic(state, action) # 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() def train_actor(self, n_iterations=1, batch_size=100, tau=0.005, replay_data=None): for it in range(n_iterations): # Sample replay buffer if replay_data is None: state, action, next_state, done, reward = self.replay_buffer.sample(batch_size) else: state, action, next_state, done, reward = replay_data # Compute actor loss actor_loss = -self.critic.q1_forward(state, self.actor(state)).mean() # Optimize the actor self.actor.optimizer.zero_grad() actor_loss.backward() self.actor.optimizer.step() # Update the frozen target models for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()): target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data) for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()): target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data) def train(self, n_iterations, batch_size=100, discount=0.99, tau=0.005, policy_noise=0.2, noise_clip=0.5, policy_freq=2): for it in range(n_iterations): # Sample replay buffer replay_data = self.replay_buffer.sample(batch_size) self.train_critic(replay_data=replay_data) # Delayed policy updates if it % policy_freq == 0: self.train_actor(replay_data=replay_data) def learn(self, total_timesteps, callback=None, log_interval=100, eval_freq=-1, n_eval_episodes=5, tb_log_name="TD3", reset_num_timesteps=True): timesteps_since_eval = 0 episode_num = 0 evaluations = [] start_time = time.time() while self.num_timesteps < total_timesteps: if callback is not None: # Only stop training if return value is False, not when it is None. if callback(locals(), globals()) is False: break episode_reward, episode_timesteps = self.collect_rollouts(self.env, n_episodes=1, action_noise_std=self.action_noise_std, deterministic=False, callback=None, start_timesteps=self.start_timesteps, num_timesteps=self.num_timesteps, replay_buffer=self.replay_buffer) episode_num += 1 self.num_timesteps += episode_timesteps timesteps_since_eval += episode_timesteps if self.num_timesteps > 0: if self.verbose > 1: print("Total T: {} Episode Num: {} Episode T: {} Reward: {}".format( self.num_timesteps, episode_num, episode_timesteps, episode_reward)) self.train(episode_timesteps, batch_size=self.batch_size, policy_freq=self.policy_freq) # Evaluate episode if 0 < eval_freq <= timesteps_since_eval: timesteps_since_eval %= eval_freq mean_reward, _ = evaluate_policy(self, self.env, n_eval_episodes) evaluations.append(mean_reward) if self.verbose > 0: print("Eval num_timesteps={}, mean_reward={:.2f}".format(self.num_timesteps, evaluations[-1])) print("FPS: {:.2f}".format(self.num_timesteps / (time.time() - start_time))) return self def save(self, path): if not path.endswith('.pth'): path += '.pth' th.save(self.policy.state_dict(), path) def load(self, path, env=None, **_kwargs): if not path.endswith('.pth'): path += '.pth' if env is not None: pass self.policy.load_state_dict(th.load(path)) self._create_aliases()