import os import time import gym from gym import spaces import torch as th import torch.nn.functional as F # Check if tensorboard is available for pytorch try: from torch.utils.tensorboard import SummaryWriter except ImportError: SummaryWriter = None import numpy as np from torchy_baselines.common.base_class import BaseRLModel from torchy_baselines.common.buffers import RolloutBuffer from torchy_baselines.common.utils import explained_variance, get_schedule_fn from torchy_baselines.common import logger from torchy_baselines.ppo.policies import PPOPolicy class PPO(BaseRLModel): """ Proximal Policy Optimization algorithm (PPO) (clip version) Paper: https://arxiv.org/abs/1707.06347 Code: This implementation borrows code from OpenAI Spinning Up (https://github.com/openai/spinningup/) https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail and and Stable Baselines (PPO2 from https://github.com/hill-a/stable-baselines) Introduction to PPO: https://spinningup.openai.com/en/latest/algorithms/ppo.html :param policy: (PPOPolicy 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) The learning rate, it can be a function of the current progress (from 1 to 0) :param n_steps: (int) 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 batch_size: (int) Minibatch size :param n_epochs: (int) Number of epoch when optimizing the surrogate loss :param gamma: (float) Discount factor :param gae_lambda: (float) Factor for trade-off of bias vs variance for Generalized Advantage Estimator :param clip_range: (float or callable) Clipping parameter, it can be a function of the current progress (from 1 to 0). :param clip_range_vf: (float or callable) Clipping parameter for the value function, it can be a function of the current progress (from 1 to 0). This is a parameter specific to the OpenAI implementation. If None is passed (default), no clipping will be done on the value function. IMPORTANT: this clipping depends on the reward scaling. :param ent_coef: (float) Entropy coefficient for the loss calculation :param vf_coef: (float) Value function coefficient for the loss calculation :param max_grad_norm: (float) The maximum value for the gradient clipping :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 target_kl: (float) Limit the KL divergence between updates, because the clipping is not enough to prevent large update see issue #213 (cf https://github.com/hill-a/stable-baselines/issues/213) By default, there is no limit on the kl div. :param tensorboard_log: (str) the log location for tensorboard (if None, no logging) :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, n_steps=2048, batch_size=64, n_epochs=10, gamma=0.99, gae_lambda=0.95, clip_range=0.2, clip_range_vf=None, ent_coef=0.0, vf_coef=0.5, max_grad_norm=0.5, use_sde=False, sde_sample_freq=-1, target_kl=None, tensorboard_log=None, create_eval_env=False, policy_kwargs=None, verbose=0, seed=0, device='auto', _init_setup_model=True): super(PPO, self).__init__(policy, env, PPOPolicy, policy_kwargs=policy_kwargs, verbose=verbose, device=device, use_sde=use_sde, sde_sample_freq=sde_sample_freq, create_eval_env=create_eval_env, support_multi_env=True, seed=seed) self.learning_rate = learning_rate self.batch_size = batch_size self.n_epochs = n_epochs self.n_steps = n_steps self.gamma = gamma self.gae_lambda = gae_lambda self.clip_range = clip_range self.clip_range_vf = clip_range_vf self.ent_coef = ent_coef self.vf_coef = vf_coef self.max_grad_norm = max_grad_norm self.rollout_buffer = None self.target_kl = target_kl self.tensorboard_log = tensorboard_log self.tb_writer = None if _init_setup_model: self._setup_model() def _setup_model(self): self._setup_learning_rate() # TODO: preprocessing: one hot vector for obs discrete state_dim = self.observation_space.shape[0] if isinstance(self.action_space, spaces.Box): # Action is a 1D vector action_dim = self.action_space.shape[0] elif isinstance(self.action_space, spaces.Discrete): # Action is a scalar action_dim = 1 # TODO: different seed for each env when n_envs > 1 if self.n_envs == 1: self.set_random_seed(self.seed) self.rollout_buffer = RolloutBuffer(self.n_steps, state_dim, action_dim, self.device, gamma=self.gamma, gae_lambda=self.gae_lambda, n_envs=self.n_envs) 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.clip_range = get_schedule_fn(self.clip_range) if self.clip_range_vf is not None: self.clip_range_vf = get_schedule_fn(self.clip_range_vf) def select_action(self, observation, deterministic=False): # Normally not needed observation = np.array(observation) with th.no_grad(): observation = th.FloatTensor(observation.reshape(1, -1)).to(self.device) return self.policy.actor_forward(observation, deterministic=deterministic) def predict(self, observation, state=None, mask=None, deterministic=False): """ 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) """ clipped_actions = self.select_action(observation, deterministic=deterministic) if isinstance(self.action_space, gym.spaces.Box): clipped_actions = np.clip(clipped_actions, self.action_space.low, self.action_space.high) return clipped_actions def collect_rollouts(self, env, rollout_buffer, n_rollout_steps=256, callback=None, obs=None): n_steps = 0 rollout_buffer.reset() # Sample new weights for the state dependent exploration # TODO: ensure episodic setting? if self.use_sde: self.policy.reset_noise(env.num_envs) while n_steps < n_rollout_steps: if self.use_sde and self.sde_sample_freq > 0 and n_steps % self.sde_sample_freq == 0: # Sample a new noise matrix self.policy.reset_noise(env.num_envs) with th.no_grad(): actions, values, log_probs = self.policy.forward(obs) actions = actions.cpu().numpy() # Rescale and perform action clipped_actions = actions # Clip the actions to avoid out of bound error if isinstance(self.action_space, gym.spaces.Box): clipped_actions = np.clip(actions, self.action_space.low, self.action_space.high) new_obs, rewards, dones, infos = env.step(clipped_actions) self._update_info_buffer(infos) n_steps += 1 if isinstance(self.action_space, gym.spaces.Discrete): # Reshape in case of discrete action actions = actions.reshape(-1, 1) rollout_buffer.add(obs, actions, rewards, dones, values, log_probs) obs = new_obs rollout_buffer.compute_returns_and_advantage(values, dones=dones) return obs def train(self, gradient_steps, batch_size=64): # Update optimizer learning rate self._update_learning_rate(self.policy.optimizer) # Compute current clip range clip_range = self.clip_range(self._current_progress) logger.logkv("clip_range", clip_range) if self.clip_range_vf is not None: clip_range_vf = self.clip_range_vf(self._current_progress) logger.logkv("clip_range_vf", clip_range_vf) for gradient_step in range(gradient_steps): approx_kl_divs = [] # Sample replay buffer for replay_data in self.rollout_buffer.get(batch_size): # Unpack obs, action, old_values, old_log_prob, advantage, return_batch = replay_data if isinstance(self.action_space, spaces.Discrete): # Convert discrete action for float to long action = action.long().flatten() # Re-sample the noise matrix because the log_std has changed # TODO: investigate why there is no issue with the gradient # if that line is commented (as in SAC) if self.use_sde: self.policy.reset_noise(batch_size) values, log_prob, entropy = self.policy.evaluate_actions(obs, action) values = values.flatten() # Normalize advantage advantage = (advantage - advantage.mean()) / (advantage.std() + 1e-8) # ratio between old and new policy, should be one at the first iteration ratio = th.exp(log_prob - old_log_prob) # clipped surrogate loss policy_loss_1 = advantage * ratio policy_loss_2 = advantage * th.clamp(ratio, 1 - clip_range, 1 + clip_range) policy_loss = -th.min(policy_loss_1, policy_loss_2).mean() if self.clip_range_vf is None: # No clipping values_pred = values else: # Clip the different between old and new value # NOTE: this depends on the reward scaling values_pred = old_values + th.clamp(values - old_values, -clip_range_vf, clip_range_vf) # Value loss using the TD(gae_lambda) target value_loss = F.mse_loss(return_batch, values_pred) # Entropy loss favor exploration 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() approx_kl_divs.append(th.mean(old_log_prob - log_prob).detach().cpu().numpy()) if self.target_kl is not None and np.mean(approx_kl_divs) > 1.5 * self.target_kl: print("Early stopping at step {} due to reaching max kl: {:.2f}".format(gradient_step, np.mean(approx_kl_divs))) break explained_var = explained_variance(self.rollout_buffer.returns.flatten().cpu().numpy(), self.rollout_buffer.values.flatten().cpu().numpy()) logger.logkv("explained_variance", explained_var) # TODO: gather stats for the entropy and other losses? logger.logkv("entropy", entropy.mean().item()) logger.logkv("policy_loss", policy_loss.item()) logger.logkv("value_loss", value_loss.item()) if hasattr(self.policy, 'log_std'): logger.logkv("std", th.exp(self.policy.log_std).mean().item()) def learn(self, total_timesteps, callback=None, log_interval=1, eval_env=None, eval_freq=-1, n_eval_episodes=5, tb_log_name="PPO", reset_num_timesteps=True): timesteps_since_eval, iteration, evaluations, obs, eval_env = self._setup_learn(eval_env) if self.tensorboard_log is not None and SummaryWriter is not None: self.tb_writer = SummaryWriter(log_dir=os.path.join(self.tensorboard_log, tb_log_name)) 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 obs = self.collect_rollouts(self.env, self.rollout_buffer, n_rollout_steps=self.n_steps, obs=obs) iteration += 1 self.num_timesteps += self.n_steps * self.n_envs timesteps_since_eval += self.n_steps * self.n_envs self._update_current_progress(self.num_timesteps, total_timesteps) # Display training infos if self.verbose >= 1 and log_interval is not None and iteration % log_interval == 0: fps = int(self.num_timesteps / (time.time() - self.start_time)) logger.logkv("iterations", iteration) if len(self.ep_info_buffer) > 0 and len(self.ep_info_buffer[0]) > 0: logger.logkv('ep_rew_mean', self.safe_mean([ep_info['r'] for ep_info in self.ep_info_buffer])) logger.logkv('ep_len_mean', self.safe_mean([ep_info['l'] for ep_info in self.ep_info_buffer])) logger.logkv("fps", fps) logger.logkv('time_elapsed', int(time.time() - self.start_time)) logger.logkv("total timesteps", self.num_timesteps) logger.dumpkvs() self.train(self.n_epochs, batch_size=self.batch_size) # Evaluate the agent timesteps_since_eval = self._eval_policy(eval_freq, eval_env, n_eval_episodes, timesteps_since_eval, deterministic=True) # For tensorboard integration # if self.tb_writer is not None: # self.tb_writer.add_scalar('Eval/reward', mean_reward, self.num_timesteps) return self def get_opt_parameters(self): """ Returns a dict of all the optimizers and their parameters :return: (dict) of optimizer names and their state_dict """ return {"opt": self.policy.optimizer.state_dict()} def load_parameters(self, load_dict, opt_params): """ Load model parameters and optimizer parameters from a dictionary load_dict should contain all keys from torch.model.state_dict() This does not load agent's hyper-parameters. :param load_dict: (dict) dict of parameters from model.state_dict() :param opt_params: (dict of dicts) dict of optimizer state_dicts """ self.policy.optimizer.load_state_dict(opt_params["opt"]) self.policy.load_state_dict(load_dict)