from typing import List, Tuple 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.buffers import ReplayBuffer from torchy_baselines.sac.policies import SACPolicy from torchy_baselines.common import logger class SAC(BaseRLModel): """ Soft Actor-Critic (SAC) Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor, This implementation borrows code from original implementation (https://github.com/haarnoja/sac) from OpenAI Spinning Up (https://github.com/openai/spinningup), from the softlearning repo (https://github.com/rail-berkeley/softlearning/) and from Stable Baselines (https://github.com/hill-a/stable-baselines) Paper: https://arxiv.org/abs/1801.01290 Introduction to SAC: https://spinningup.openai.com/en/latest/algorithms/sac.html Note: we use double q target and not value target as discussed in https://github.com/hill-a/stable-baselines/issues/270 :param policy: (SACPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, ...) :param env: (Gym environment 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 batch_size: (int) Minibatch size for each gradient update :param tau: (float) the soft update coefficient ("polyak update", between 0 and 1) :param ent_coef: (str or float) Entropy regularization coefficient. (Equivalent to inverse of reward scale in the original SAC paper.) Controlling exploration/exploitation trade-off. Set it to 'auto' to learn it automatically (and 'auto_0.1' for using 0.1 as initial value) :param learning_starts: (int) how many steps of the model to collect transitions for before learning starts :param target_update_interval: (int) update the target network every `target_network_update_freq` steps. :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 target_entropy: (str or float) target entropy when learning `ent_coef` (`ent_coef = 'auto'`) :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 gamma: (float) the discount factor :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 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 none, 1 training information, 2 tensorflow 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, env, learning_rate=3e-4, buffer_size=int(1e6), learning_starts=100, batch_size=256, tau=0.005, ent_coef='auto', target_update_interval=1, train_freq=1, gradient_steps=1, n_episodes_rollout=-1, target_entropy='auto', action_noise=None, gamma=0.99, use_sde=False, sde_sample_freq=-1, tensorboard_log=None, create_eval_env=False, policy_kwargs=None, verbose=0, seed=0, device='auto', _init_setup_model=True): super(SAC, self).__init__(policy, env, SACPolicy, policy_kwargs, verbose, device, create_eval_env=create_eval_env, seed=seed, use_sde=use_sde, sde_sample_freq=sde_sample_freq) self.learning_rate = learning_rate self.target_entropy = target_entropy self.log_ent_coef = None self.target_update_interval = target_update_interval self.buffer_size = buffer_size # In the original paper, same learning rate is used for all networks self.learning_rate = learning_rate self.learning_starts = learning_starts self.batch_size = batch_size self.tau = tau # Entropy coefficient / Entropy temperature # Inverse of the reward scale self.ent_coef = ent_coef self.target_update_interval = target_update_interval self.train_freq = train_freq self.gradient_steps = gradient_steps self.n_episodes_rollout = n_episodes_rollout self.action_noise = action_noise self.gamma = gamma self.ent_coef_optimizer = None if _init_setup_model: self._setup_model() def _setup_model(self): self._setup_learning_rate() obs_dim, action_dim = self.observation_space.shape[0], self.action_space.shape[0] if self.seed is not None: self.set_random_seed(self.seed) # Target entropy is used when learning the entropy coefficient if self.target_entropy == 'auto': # automatically set target entropy if needed self.target_entropy = -np.prod(self.env.action_space.shape).astype(np.float32) else: # Force conversion # this will also throw an error for unexpected string self.target_entropy = float(self.target_entropy) # The entropy coefficient or entropy can be learned automatically # see Automating Entropy Adjustment for Maximum Entropy RL section # of https://arxiv.org/abs/1812.05905 if isinstance(self.ent_coef, str) and self.ent_coef.startswith('auto'): # Default initial value of ent_coef when learned init_value = 1.0 if '_' in self.ent_coef: init_value = float(self.ent_coef.split('_')[1]) assert init_value > 0., "The initial value of ent_coef must be greater than 0" # Note: we optimize the log of the entropy coeff which is slightly different from the paper # as discussed in https://github.com/rail-berkeley/softlearning/issues/37 self.log_ent_coef = th.log(th.ones(1, device=self.device) * init_value).requires_grad_(True) self.ent_coef_optimizer = th.optim.Adam([self.log_ent_coef], lr=self.learning_rate(1)) else: # Force conversion to float # this will throw an error if a malformed string (different from 'auto') # is passed self.ent_coef_tensor = th.tensor(float(self.ent_coef)).to(self.device) self.replay_buffer = ReplayBuffer(self.buffer_size, obs_dim, action_dim, self.device) self.policy = self.policy_class(self.observation_space, self.action_space, self.learning_rate, use_sde=self.use_sde, device=self.device, **self.policy_kwargs) self.policy = self.policy.to(self.device) self._create_aliases() def _create_aliases(self): self.actor = self.policy.actor 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() 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.unscale_action(self.select_action(observation)) def train(self, gradient_steps: int, batch_size: int = 64): # Update optimizers learning rate optimizers = [self.actor.optimizer, self.critic.optimizer] if self.ent_coef_optimizer is not None: optimizers += [self.ent_coef_optimizer] self._update_learning_rate(optimizers) ent_coef_loss, ent_coef = th.zeros(1), th.zeros(1) actor_loss, critic_loss = th.zeros(1), th.zeros(1) for gradient_step in range(gradient_steps): # Sample replay buffer replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env) obs, action_batch, next_obs, done, reward = replay_data # Two options: retain_graph=True in the actor_loss.backward() # or sample again the noise matrix # otherwise the intermediate step `std = th.exp(log_std)` # is lost and we cannot backpropagate through again # anyway, we need to sample because `log_std` may have changed between two gradient steps if self.use_sde: self.actor.reset_noise(batch_size=batch_size) # self.actor.reset_noise() # Action by the current actor for the sampled state action_pi, log_prob = self.actor.action_log_prob(obs) log_prob = log_prob.reshape(-1, 1) ent_coef_loss = None if self.ent_coef_optimizer is not None: # Important: detach the variable from the graph # so we don't change it with other losses # see https://github.com/rail-berkeley/softlearning/issues/60 ent_coef = th.exp(self.log_ent_coef.detach()) ent_coef_loss = -(self.log_ent_coef * (log_prob + self.target_entropy).detach()).mean() else: ent_coef = self.ent_coef_tensor # Optimize entropy coefficient, also called # entropy temperature or alpha in the paper if ent_coef_loss is not None: self.ent_coef_optimizer.zero_grad() ent_coef_loss.backward() self.ent_coef_optimizer.step() with th.no_grad(): # if self.use_sde: # self.actor.reset_noise(batch_size=batch_size) # Select action according to policy next_action, next_log_prob = self.actor.action_log_prob(next_obs) # Compute the target Q value target_q1, target_q2 = self.critic_target(next_obs, next_action) target_q = th.min(target_q1, target_q2) target_q = reward + (1 - done) * self.gamma * target_q # td error + entropy term q_backup = target_q - ent_coef * next_log_prob.reshape(-1, 1) # Get current Q estimates # using action from the replay buffer current_q1, current_q2 = self.critic(obs, action_batch) # Compute critic loss critic_loss = 0.5 * (F.mse_loss(current_q1, q_backup) + F.mse_loss(current_q2, q_backup)) # Optimize the critic self.critic.optimizer.zero_grad() critic_loss.backward() self.critic.optimizer.step() # Compute actor loss # Alternative: actor_loss = th.mean(log_prob - qf1_pi) qf1_pi, qf2_pi = self.critic.forward(obs, action_pi) min_qf_pi = th.min(qf1_pi, qf2_pi) actor_loss = (ent_coef * log_prob - min_qf_pi).mean() # Optimize the actor self.actor.optimizer.zero_grad() actor_loss.backward() self.actor.optimizer.step() # Update target networks if gradient_step % self.target_update_interval == 0: 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) # TODO: average logger.logkv("ent_coef", ent_coef.item()) logger.logkv("actor_loss", actor_loss.item()) logger.logkv("critic_loss", critic_loss.item()) if ent_coef_loss is not None: logger.logkv("ent_coef_loss", ent_coef_loss.item()) def learn(self, total_timesteps, callback=None, log_interval=4, eval_env=None, eval_freq=-1, n_eval_episodes=5, tb_log_name="SAC", eval_log_path=None, reset_num_timesteps=True): episode_num, obs, 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, deterministic=False, callback=callback, learning_starts=self.learning_starts, replay_buffer=self.replay_buffer, obs=obs, episode_num=episode_num, log_interval=log_interval) # Unpack episode_reward, episode_timesteps, n_episodes, obs, continue_training = rollout if continue_training is False: break episode_num += n_episodes self._update_current_progress(self.num_timesteps, total_timesteps) if self.num_timesteps > 0 and self.num_timesteps > self.learning_starts: gradient_steps = self.gradient_steps if self.gradient_steps > 0 else episode_timesteps self.train(gradient_steps, batch_size=self.batch_size) 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(SAC, self).excluded_save_params() + ["actor", "critic", "critic_target"] def get_torch_variables(self) -> Tuple[List[str], List[str]]: """ cf base class """ state_dicts = ["policy", "actor.optimizer", "critic.optimizer"] saved_tensors = ['log_ent_coef'] if self.ent_coef_optimizer is not None: state_dicts.append('ent_coef_optimizer') else: saved_tensors.append('ent_coef_tensor') return state_dicts, saved_tensors