Source code for stable_baselines.td3.td3

import sys
import time
import warnings

import numpy as np
import tensorflow as tf

from stable_baselines import logger
from stable_baselines.common import tf_util, OffPolicyRLModel, SetVerbosity, TensorboardWriter
from stable_baselines.common.vec_env import VecEnv
from stable_baselines.common.math_util import safe_mean, unscale_action, scale_action
from stable_baselines.common.schedules import get_schedule_fn
from stable_baselines.common.buffers import ReplayBuffer
from stable_baselines.td3.policies import TD3Policy

[docs]class TD3(OffPolicyRLModel): """ Twin Delayed DDPG (TD3) Addressing Function Approximation Error in Actor-Critic Methods. Original implementation: Paper: Introduction to TD3: :param policy: (TD3Policy or str) The policy model to use (MlpPolicy, CnnPolicy, LnMlpPolicy, ...) :param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str) :param gamma: (float) the discount factor :param learning_rate: (float or callable) learning rate for adam optimizer, the same learning rate will be used for all networks (Q-Values and Actor networks) 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" of the target networks, between 0 and 1) :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 action_noise: (ActionNoise) the action noise type. Cf DDPG for the different action noise type. :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 train_freq: (int) Update the model every `train_freq` steps. :param learning_starts: (int) how many steps of the model to collect transitions for before learning starts :param gradient_steps: (int) How many gradient update after each step :param random_exploration: (float) Probability of taking a random action (as in an epsilon-greedy strategy) This is not needed for TD3 normally but can help exploring when using HER + TD3. This hack was present in the original OpenAI Baselines repo (DDPG + HER) :param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug :param tensorboard_log: (str) the log location for tensorboard (if None, no logging) :param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance :param policy_kwargs: (dict) additional arguments to be passed to the policy on creation :param full_tensorboard_log: (bool) enable additional logging when using tensorboard Note: this has no effect on TD3 logging for now :param seed: (int) Seed for the pseudo-random generators (python, numpy, tensorflow). If None (default), use random seed. Note that if you want completely deterministic results, you must set `n_cpu_tf_sess` to 1. :param n_cpu_tf_sess: (int) The number of threads for TensorFlow operations If None, the number of cpu of the current machine will be used. """ def __init__(self, policy, env, gamma=0.99, learning_rate=3e-4, buffer_size=50000, learning_starts=100, train_freq=100, gradient_steps=100, batch_size=128, tau=0.005, policy_delay=2, action_noise=None, target_policy_noise=0.2, target_noise_clip=0.5, random_exploration=0.0, verbose=0, tensorboard_log=None, _init_setup_model=True, policy_kwargs=None, full_tensorboard_log=False, seed=None, n_cpu_tf_sess=None): super(TD3, self).__init__(policy=policy, env=env, replay_buffer=None, verbose=verbose, policy_base=TD3Policy, requires_vec_env=False, policy_kwargs=policy_kwargs, seed=seed, n_cpu_tf_sess=n_cpu_tf_sess) self.buffer_size = buffer_size self.learning_rate = learning_rate self.learning_starts = learning_starts self.train_freq = train_freq self.batch_size = batch_size self.tau = tau self.gradient_steps = gradient_steps self.gamma = gamma self.action_noise = action_noise self.random_exploration = random_exploration self.policy_delay = policy_delay self.target_noise_clip = target_noise_clip self.target_policy_noise = target_policy_noise self.graph = None self.replay_buffer = None self.sess = None self.tensorboard_log = tensorboard_log self.verbose = verbose self.params = None self.summary = None self.policy_tf = None self.full_tensorboard_log = full_tensorboard_log self.obs_target = None self.target_policy_tf = None self.actions_ph = None self.rewards_ph = None self.terminals_ph = None self.observations_ph = None self.action_target = None self.next_observations_ph = None self.step_ops = None self.target_ops = None self.infos_names = None self.target_params = None self.learning_rate_ph = None self.processed_obs_ph = None self.processed_next_obs_ph = None self.policy_out = None self.policy_train_op = None self.policy_loss = None if _init_setup_model: self.setup_model() def _get_pretrain_placeholders(self): policy = self.policy_tf # Rescale policy_out = unscale_action(self.action_space, self.policy_out) return policy.obs_ph, self.actions_ph, policy_out
[docs] def setup_model(self): with SetVerbosity(self.verbose): self.graph = tf.Graph() with self.graph.as_default(): self.set_random_seed(self.seed) self.sess = tf_util.make_session(num_cpu=self.n_cpu_tf_sess, graph=self.graph) self.replay_buffer = ReplayBuffer(self.buffer_size) with tf.variable_scope("input", reuse=False): # Create policy and target TF objects self.policy_tf = self.policy(self.sess, self.observation_space, self.action_space, **self.policy_kwargs) self.target_policy_tf = self.policy(self.sess, self.observation_space, self.action_space, **self.policy_kwargs) # Initialize Placeholders self.observations_ph = self.policy_tf.obs_ph # Normalized observation for pixels self.processed_obs_ph = self.policy_tf.processed_obs self.next_observations_ph = self.target_policy_tf.obs_ph self.processed_next_obs_ph = self.target_policy_tf.processed_obs self.action_target = self.target_policy_tf.action_ph self.terminals_ph = tf.placeholder(tf.float32, shape=(None, 1), name='terminals') self.rewards_ph = tf.placeholder(tf.float32, shape=(None, 1), name='rewards') self.actions_ph = tf.placeholder(tf.float32, shape=(None,) + self.action_space.shape, name='actions') self.learning_rate_ph = tf.placeholder(tf.float32, [], name="learning_rate_ph") with tf.variable_scope("model", reuse=False): # Create the policy self.policy_out = policy_out = self.policy_tf.make_actor(self.processed_obs_ph) # Use two Q-functions to improve performance by reducing overestimation bias qf1, qf2 = self.policy_tf.make_critics(self.processed_obs_ph, self.actions_ph) # Q value when following the current policy qf1_pi, _ = self.policy_tf.make_critics(self.processed_obs_ph, policy_out, reuse=True) with tf.variable_scope("target", reuse=False): # Create target networks target_policy_out = self.target_policy_tf.make_actor(self.processed_next_obs_ph) # Target policy smoothing, by adding clipped noise to target actions target_noise = tf.random_normal(tf.shape(target_policy_out), stddev=self.target_policy_noise) target_noise = tf.clip_by_value(target_noise, -self.target_noise_clip, self.target_noise_clip) # Clip the noisy action to remain in the bounds [-1, 1] (output of a tanh) noisy_target_action = tf.clip_by_value(target_policy_out + target_noise, -1, 1) # Q values when following the target policy qf1_target, qf2_target = self.target_policy_tf.make_critics(self.processed_next_obs_ph, noisy_target_action) with tf.variable_scope("loss", reuse=False): # Take the min of the two target Q-Values (clipped Double-Q Learning) min_qf_target = tf.minimum(qf1_target, qf2_target) # Targets for Q value regression q_backup = tf.stop_gradient( self.rewards_ph + (1 - self.terminals_ph) * self.gamma * min_qf_target ) # Compute Q-Function loss qf1_loss = tf.reduce_mean((q_backup - qf1) ** 2) qf2_loss = tf.reduce_mean((q_backup - qf2) ** 2) qvalues_losses = qf1_loss + qf2_loss # Policy loss: maximise q value self.policy_loss = policy_loss = -tf.reduce_mean(qf1_pi) # Policy train op # will be called only every n training steps, # where n is the policy delay policy_optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate_ph) policy_train_op = policy_optimizer.minimize(policy_loss, var_list=tf_util.get_trainable_vars('model/pi')) self.policy_train_op = policy_train_op # Q Values optimizer qvalues_optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate_ph) qvalues_params = tf_util.get_trainable_vars('model/values_fn/') # Q Values and policy target params source_params = tf_util.get_trainable_vars("model/") target_params = tf_util.get_trainable_vars("target/") # Polyak averaging for target variables self.target_ops = [ tf.assign(target, (1 - self.tau) * target + self.tau * source) for target, source in zip(target_params, source_params) ] # Initializing target to match source variables target_init_op = [ tf.assign(target, source) for target, source in zip(target_params, source_params) ] train_values_op = qvalues_optimizer.minimize(qvalues_losses, var_list=qvalues_params) self.infos_names = ['qf1_loss', 'qf2_loss'] # All ops to call during one training step self.step_ops = [qf1_loss, qf2_loss, qf1, qf2, train_values_op] # Monitor losses and entropy in tensorboard tf.summary.scalar('policy_loss', policy_loss) tf.summary.scalar('qf1_loss', qf1_loss) tf.summary.scalar('qf2_loss', qf2_loss) tf.summary.scalar('learning_rate', tf.reduce_mean(self.learning_rate_ph)) # Retrieve parameters that must be saved self.params = tf_util.get_trainable_vars("model") self.target_params = tf_util.get_trainable_vars("target/") # Initialize Variables and target network with self.sess.as_default(): self.summary = tf.summary.merge_all()
def _train_step(self, step, writer, learning_rate, update_policy): # Sample a batch from the replay buffer batch = self.replay_buffer.sample(self.batch_size) batch_obs, batch_actions, batch_rewards, batch_next_obs, batch_dones = batch feed_dict = { self.observations_ph: batch_obs, self.actions_ph: batch_actions, self.next_observations_ph: batch_next_obs, self.rewards_ph: batch_rewards.reshape(self.batch_size, -1), self.terminals_ph: batch_dones.reshape(self.batch_size, -1), self.learning_rate_ph: learning_rate } step_ops = self.step_ops if update_policy: # Update policy and target networks step_ops = step_ops + [self.policy_train_op, self.target_ops, self.policy_loss] # Do one gradient step # and optionally compute log for tensorboard if writer is not None: out =[self.summary] + step_ops, feed_dict) summary = out.pop(0) writer.add_summary(summary, step) else: out =, feed_dict) # Unpack to monitor losses qf1_loss, qf2_loss, *_values = out return qf1_loss, qf2_loss
[docs] def learn(self, total_timesteps, callback=None, log_interval=4, tb_log_name="TD3", reset_num_timesteps=True, replay_wrapper=None): new_tb_log = self._init_num_timesteps(reset_num_timesteps) callback = self._init_callback(callback) if replay_wrapper is not None: self.replay_buffer = replay_wrapper(self.replay_buffer) with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name, new_tb_log) \ as writer: self._setup_learn() # Transform to callable if needed self.learning_rate = get_schedule_fn(self.learning_rate) # Initial learning rate current_lr = self.learning_rate(1) start_time = time.time() episode_rewards = [0.0] episode_successes = [] if self.action_noise is not None: self.action_noise.reset() obs = self.env.reset() n_updates = 0 infos_values = [] callback.on_training_start(locals(), globals()) callback.on_rollout_start() for step in range(total_timesteps): # Before training starts, randomly sample actions # from a uniform distribution for better exploration. # Afterwards, use the learned policy # if random_exploration is set to 0 (normal setting) if self.num_timesteps < self.learning_starts or np.random.rand() < self.random_exploration: # actions sampled from action space are from range specific to the environment # but algorithm operates on tanh-squashed actions therefore simple scaling is used unscaled_action = self.env.action_space.sample() action = scale_action(self.action_space, unscaled_action) else: action = self.policy_tf.step(obs[None]).flatten() # Add noise to the action, as the policy # is deterministic, this is required for exploration if self.action_noise is not None: action = np.clip(action + self.action_noise(), -1, 1) # Rescale from [-1, 1] to the correct bounds unscaled_action = unscale_action(self.action_space, action) assert action.shape == self.env.action_space.shape new_obs, reward, done, info = self.env.step(unscaled_action) self.num_timesteps += 1 # Only stop training if return value is False, not when it is None. This is for backwards # compatibility with callbacks that have no return statement. if callback.on_step() is False: break # Store transition in the replay buffer. self.replay_buffer.add(obs, action, reward, new_obs, float(done)) obs = new_obs # Retrieve reward and episode length if using Monitor wrapper maybe_ep_info = info.get('episode') if maybe_ep_info is not None: self.ep_info_buf.extend([maybe_ep_info]) if writer is not None: # Write reward per episode to tensorboard ep_reward = np.array([reward]).reshape((1, -1)) ep_done = np.array([done]).reshape((1, -1)) tf_util.total_episode_reward_logger(self.episode_reward, ep_reward, ep_done, writer, self.num_timesteps) if step % self.train_freq == 0: callback.on_rollout_end() mb_infos_vals = [] # Update policy, critics and target networks for grad_step in range(self.gradient_steps): # Break if the warmup phase is not over # or if there are not enough samples in the replay buffer if not self.replay_buffer.can_sample(self.batch_size) \ or self.num_timesteps < self.learning_starts: break n_updates += 1 # Compute current learning_rate frac = 1.0 - step / total_timesteps current_lr = self.learning_rate(frac) # Update policy and critics (q functions) # Note: the policy is updated less frequently than the Q functions # this is controlled by the `policy_delay` parameter mb_infos_vals.append( self._train_step(step, writer, current_lr, (step + grad_step) % self.policy_delay == 0)) # Log losses and entropy, useful for monitor training if len(mb_infos_vals) > 0: infos_values = np.mean(mb_infos_vals, axis=0) callback.on_rollout_start() episode_rewards[-1] += reward if done: if self.action_noise is not None: self.action_noise.reset() if not isinstance(self.env, VecEnv): obs = self.env.reset() episode_rewards.append(0.0) maybe_is_success = info.get('is_success') if maybe_is_success is not None: episode_successes.append(float(maybe_is_success)) if len(episode_rewards[-101:-1]) == 0: mean_reward = -np.inf else: mean_reward = round(float(np.mean(episode_rewards[-101:-1])), 1) num_episodes = len(episode_rewards) # Display training infos if self.verbose >= 1 and done and log_interval is not None and len(episode_rewards) % log_interval == 0: fps = int(step / (time.time() - start_time)) logger.logkv("episodes", num_episodes) logger.logkv("mean 100 episode reward", mean_reward) if len(self.ep_info_buf) > 0 and len(self.ep_info_buf[0]) > 0: logger.logkv('ep_rewmean', safe_mean([ep_info['r'] for ep_info in self.ep_info_buf])) logger.logkv('eplenmean', safe_mean([ep_info['l'] for ep_info in self.ep_info_buf])) logger.logkv("n_updates", n_updates) logger.logkv("current_lr", current_lr) logger.logkv("fps", fps) logger.logkv('time_elapsed', int(time.time() - start_time)) if len(episode_successes) > 0: logger.logkv("success rate", np.mean(episode_successes[-100:])) if len(infos_values) > 0: for (name, val) in zip(self.infos_names, infos_values): logger.logkv(name, val) logger.logkv("total timesteps", self.num_timesteps) logger.dumpkvs() # Reset infos: infos_values = [] callback.on_training_end() return self
[docs] def action_probability(self, observation, state=None, mask=None, actions=None, logp=False): _ = np.array(observation) if actions is not None: raise ValueError("Error: TD3 does not have action probabilities.") # here there are no action probabilities, as DDPG does not use a probability distribution warnings.warn("Warning: action probability is meaningless for TD3. Returning None") return None
[docs] def predict(self, observation, state=None, mask=None, deterministic=True): observation = np.array(observation) vectorized_env = self._is_vectorized_observation(observation, self.observation_space) observation = observation.reshape((-1,) + self.observation_space.shape) actions = self.policy_tf.step(observation) if self.action_noise is not None and not deterministic: actions = np.clip(actions + self.action_noise(), -1, 1) actions = actions.reshape((-1,) + self.action_space.shape) # reshape to the correct action shape actions = unscale_action(self.action_space, actions) # scale the output for the prediction if not vectorized_env: actions = actions[0] return actions, None
[docs] def get_parameter_list(self): return (self.params + self.target_params)
[docs] def save(self, save_path, cloudpickle=False): data = { "learning_rate": self.learning_rate, "buffer_size": self.buffer_size, "learning_starts": self.learning_starts, "train_freq": self.train_freq, "batch_size": self.batch_size, "tau": self.tau, # Should we also store the replay buffer? # this may lead to high memory usage # with all transition inside # "replay_buffer": self.replay_buffer "policy_delay": self.policy_delay, "target_noise_clip": self.target_noise_clip, "target_policy_noise": self.target_policy_noise, "gamma": self.gamma, "verbose": self.verbose, "observation_space": self.observation_space, "action_space": self.action_space, "policy": self.policy, "n_envs": self.n_envs, "n_cpu_tf_sess": self.n_cpu_tf_sess, "seed": self.seed, "action_noise": self.action_noise, "random_exploration": self.random_exploration, "_vectorize_action": self._vectorize_action, "policy_kwargs": self.policy_kwargs } params_to_save = self.get_parameters() self._save_to_file(save_path, data=data, params=params_to_save, cloudpickle=cloudpickle)