Source code for stable_baselines.ddpg.ddpg

from functools import reduce
import os
import time
from collections import deque
import pickle
import warnings

import gym
import numpy as np
import tensorflow as tf
import tensorflow.contrib as tc
from mpi4py import MPI

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.mpi_adam import MpiAdam
from stable_baselines.common.buffers import ReplayBuffer
from stable_baselines.common.math_util import unscale_action, scale_action
from stable_baselines.common.mpi_running_mean_std import RunningMeanStd
from stable_baselines.ddpg.policies import DDPGPolicy

def normalize(tensor, stats):
    normalize a tensor using a running mean and std

    :param tensor: (TensorFlow Tensor) the input tensor
    :param stats: (RunningMeanStd) the running mean and std of the input to normalize
    :return: (TensorFlow Tensor) the normalized tensor
    if stats is None:
        return tensor
    return (tensor - stats.mean) / stats.std

def denormalize(tensor, stats):
    denormalize a tensor using a running mean and std

    :param tensor: (TensorFlow Tensor) the normalized tensor
    :param stats: (RunningMeanStd) the running mean and std of the input to normalize
    :return: (TensorFlow Tensor) the restored tensor
    if stats is None:
        return tensor
    return tensor * stats.std + stats.mean

def reduce_std(tensor, axis=None, keepdims=False):
    get the standard deviation of a Tensor

    :param tensor: (TensorFlow Tensor) the input tensor
    :param axis: (int or [int]) the axis to itterate the std over
    :param keepdims: (bool) keep the other dimensions the same
    :return: (TensorFlow Tensor) the std of the tensor
    return tf.sqrt(reduce_var(tensor, axis=axis, keepdims=keepdims))

def reduce_var(tensor, axis=None, keepdims=False):
    get the variance of a Tensor

    :param tensor: (TensorFlow Tensor) the input tensor
    :param axis: (int or [int]) the axis to itterate the variance over
    :param keepdims: (bool) keep the other dimensions the same
    :return: (TensorFlow Tensor) the variance of the tensor
    tensor_mean = tf.reduce_mean(tensor, axis=axis, keepdims=True)
    devs_squared = tf.square(tensor - tensor_mean)
    return tf.reduce_mean(devs_squared, axis=axis, keepdims=keepdims)

def get_target_updates(_vars, target_vars, tau, verbose=0):
    get target update operations

    :param _vars: ([TensorFlow Tensor]) the initial variables
    :param target_vars: ([TensorFlow Tensor]) the target variables
    :param tau: (float) the soft update coefficient (keep old values, between 0 and 1)
    :param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
    :return: (TensorFlow Operation, TensorFlow Operation) initial update, soft update
    if verbose >= 2:'setting up target updates ...')
    soft_updates = []
    init_updates = []
    assert len(_vars) == len(target_vars)
    for var, target_var in zip(_vars, target_vars):
        if verbose >= 2:
  '  {} <- {}'.format(,
        init_updates.append(tf.assign(target_var, var))
        soft_updates.append(tf.assign(target_var, (1. - tau) * target_var + tau * var))
    assert len(init_updates) == len(_vars)
    assert len(soft_updates) == len(_vars)

def get_perturbable_vars(scope):
    Get the trainable variables that can be perturbed when using
    parameter noise.

    :param scope: (str) tensorflow scope of the variables
    :return: ([tf.Variables])
    return [var for var in tf_util.get_trainable_vars(scope) if 'LayerNorm' not in]

def get_perturbed_actor_updates(actor, perturbed_actor, param_noise_stddev, verbose=0):
    Get the actor update, with noise.

    :param actor: (str) the actor
    :param perturbed_actor: (str) the pertubed actor
    :param param_noise_stddev: (float) the std of the parameter noise
    :param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
    :return: (TensorFlow Operation) the update function
    assert len(tf_util.get_globals_vars(actor)) == len(tf_util.get_globals_vars(perturbed_actor))
    assert len(get_perturbable_vars(actor)) == len(get_perturbable_vars(perturbed_actor))

    updates = []
    for var, perturbed_var in zip(tf_util.get_globals_vars(actor), tf_util.get_globals_vars(perturbed_actor)):
        if var in get_perturbable_vars(actor):
            if verbose >= 2:
      '  {} <- {} + noise'.format(,
            # Add Gaussian noise to the parameter
                                     var + tf.random_normal(tf.shape(var), mean=0., stddev=param_noise_stddev)))
            if verbose >= 2:
      '  {} <- {}'.format(,
            updates.append(tf.assign(perturbed_var, var))
    assert len(updates) == len(tf_util.get_globals_vars(actor))

[docs]class DDPG(OffPolicyRLModel): """ Deep Deterministic Policy Gradient (DDPG) model DDPG: :param policy: (DDPGPolicy 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 memory_policy: (ReplayBuffer) the replay buffer (if None, default to baselines.deepq.replay_buffer.ReplayBuffer) .. deprecated:: 2.6.0 This parameter will be removed in a future version :param eval_env: (Gym Environment) the evaluation environment (can be None) :param nb_train_steps: (int) the number of training steps :param nb_rollout_steps: (int) the number of rollout steps :param nb_eval_steps: (int) the number of evaluation steps :param param_noise: (AdaptiveParamNoiseSpec) the parameter noise type (can be None) :param action_noise: (ActionNoise) the action noise type (can be None) :param param_noise_adaption_interval: (int) apply param noise every N steps :param tau: (float) the soft update coefficient (keep old values, between 0 and 1) :param normalize_returns: (bool) should the critic output be normalized :param enable_popart: (bool) enable pop-art normalization of the critic output (, normalize_returns must be set to True. :param normalize_observations: (bool) should the observation be normalized :param batch_size: (int) the size of the batch for learning the policy :param observation_range: (tuple) the bounding values for the observation :param return_range: (tuple) the bounding values for the critic output :param critic_l2_reg: (float) l2 regularizer coefficient :param actor_lr: (float) the actor learning rate :param critic_lr: (float) the critic learning rate :param clip_norm: (float) clip the gradients (disabled if None) :param reward_scale: (float) the value the reward should be scaled by :param render: (bool) enable rendering of the environment :param render_eval: (bool) enable rendering of the evaluation environment :param memory_limit: (int) the max number of transitions to store, size of the replay buffer .. deprecated:: 2.6.0 Use `buffer_size` instead. :param buffer_size: (int) the max number of transitions to store, size of the replay buffer :param random_exploration: (float) Probability of taking a random action (as in an epsilon-greedy strategy) This is not needed for DDPG normally but can help exploring when using HER + DDPG. 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 WARNING: this logging can take a lot of space quickly :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, memory_policy=None, eval_env=None, nb_train_steps=50, nb_rollout_steps=100, nb_eval_steps=100, param_noise=None, action_noise=None, normalize_observations=False, tau=0.001, batch_size=128, param_noise_adaption_interval=50, normalize_returns=False, enable_popart=False, observation_range=(-5., 5.), critic_l2_reg=0., return_range=(-np.inf, np.inf), actor_lr=1e-4, critic_lr=1e-3, clip_norm=None, reward_scale=1., render=False, render_eval=False, memory_limit=None, buffer_size=50000, 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=1): super(DDPG, self).__init__(policy=policy, env=env, replay_buffer=None, verbose=verbose, policy_base=DDPGPolicy, requires_vec_env=False, policy_kwargs=policy_kwargs, seed=seed, n_cpu_tf_sess=n_cpu_tf_sess) # Parameters. self.gamma = gamma self.tau = tau # TODO: remove this param in v3.x.x if memory_policy is not None: warnings.warn("memory_policy will be removed in a future version (v3.x.x) " "it is now ignored and replaced with ReplayBuffer", DeprecationWarning) if memory_limit is not None: warnings.warn("memory_limit will be removed in a future version (v3.x.x) " "use buffer_size instead", DeprecationWarning) buffer_size = memory_limit self.normalize_observations = normalize_observations self.normalize_returns = normalize_returns self.action_noise = action_noise self.param_noise = param_noise self.return_range = return_range self.observation_range = observation_range self.actor_lr = actor_lr self.critic_lr = critic_lr self.clip_norm = clip_norm self.enable_popart = enable_popart self.reward_scale = reward_scale self.batch_size = batch_size self.critic_l2_reg = critic_l2_reg self.eval_env = eval_env self.render = render self.render_eval = render_eval self.nb_eval_steps = nb_eval_steps self.param_noise_adaption_interval = param_noise_adaption_interval self.nb_train_steps = nb_train_steps self.nb_rollout_steps = nb_rollout_steps self.memory_limit = memory_limit self.buffer_size = buffer_size self.tensorboard_log = tensorboard_log self.full_tensorboard_log = full_tensorboard_log self.random_exploration = random_exploration # init self.graph = None self.stats_sample = None self.replay_buffer = None self.policy_tf = None self.target_init_updates = None self.target_soft_updates = None self.critic_loss = None self.critic_grads = None self.critic_optimizer = None self.sess = None self.stats_ops = None self.stats_names = None self.perturbed_actor_tf = None self.perturb_policy_ops = None self.perturb_adaptive_policy_ops = None self.adaptive_policy_distance = None self.actor_loss = None self.actor_grads = None self.actor_optimizer = None self.old_std = None self.old_mean = None self.renormalize_q_outputs_op = None self.obs_rms = None self.ret_rms = None self.target_policy = None self.actor_tf = None self.normalized_critic_tf = None self.critic_tf = None self.normalized_critic_with_actor_tf = None self.critic_with_actor_tf = None self.target_q = None self.obs_train = None self.action_train_ph = None self.obs_target = None self.action_target = None self.obs_noise = None self.action_noise_ph = None self.obs_adapt_noise = None self.action_adapt_noise = None self.terminals_ph = None self.rewards = None self.actions = None self.critic_target = None self.param_noise_stddev = None self.param_noise_actor = None self.adaptive_param_noise_actor = None self.params = None self.summary = None self.tb_seen_steps = None self.target_params = None self.obs_rms_params = None self.ret_rms_params = None if _init_setup_model: self.setup_model() def _get_pretrain_placeholders(self): policy = self.policy_tf # Rescale deterministic_action = unscale_action(self.action_space, self.actor_tf) return policy.obs_ph, self.actions, deterministic_action
[docs] def setup_model(self): with SetVerbosity(self.verbose): assert isinstance(self.action_space, gym.spaces.Box), \ "Error: DDPG cannot output a {} action space, only spaces.Box is supported.".format(self.action_space) assert issubclass(self.policy, DDPGPolicy), "Error: the input policy for the DDPG model must be " \ "an instance of DDPGPolicy." 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): # Observation normalization. if self.normalize_observations: with tf.variable_scope('obs_rms'): self.obs_rms = RunningMeanStd(shape=self.observation_space.shape) else: self.obs_rms = None # Return normalization. if self.normalize_returns: with tf.variable_scope('ret_rms'): self.ret_rms = RunningMeanStd() else: self.ret_rms = None self.policy_tf = self.policy(self.sess, self.observation_space, self.action_space, 1, 1, None, **self.policy_kwargs) # Create target networks. self.target_policy = self.policy(self.sess, self.observation_space, self.action_space, 1, 1, None, **self.policy_kwargs) self.obs_target = self.target_policy.obs_ph self.action_target = self.target_policy.action_ph normalized_obs = tf.clip_by_value(normalize(self.policy_tf.processed_obs, self.obs_rms), self.observation_range[0], self.observation_range[1]) normalized_next_obs = tf.clip_by_value(normalize(self.target_policy.processed_obs, self.obs_rms), self.observation_range[0], self.observation_range[1]) if self.param_noise is not None: # Configure perturbed actor. self.param_noise_actor = self.policy(self.sess, self.observation_space, self.action_space, 1, 1, None, **self.policy_kwargs) self.obs_noise = self.param_noise_actor.obs_ph self.action_noise_ph = self.param_noise_actor.action_ph # Configure separate copy for stddev adoption. self.adaptive_param_noise_actor = self.policy(self.sess, self.observation_space, self.action_space, 1, 1, None, **self.policy_kwargs) self.obs_adapt_noise = self.adaptive_param_noise_actor.obs_ph self.action_adapt_noise = self.adaptive_param_noise_actor.action_ph # Inputs. self.obs_train = self.policy_tf.obs_ph self.action_train_ph = self.policy_tf.action_ph self.terminals_ph = tf.placeholder(tf.float32, shape=(None, 1), name='terminals') self.rewards = tf.placeholder(tf.float32, shape=(None, 1), name='rewards') self.actions = tf.placeholder(tf.float32, shape=(None,) + self.action_space.shape, name='actions') self.critic_target = tf.placeholder(tf.float32, shape=(None, 1), name='critic_target') self.param_noise_stddev = tf.placeholder(tf.float32, shape=(), name='param_noise_stddev') # Create networks and core TF parts that are shared across setup parts. with tf.variable_scope("model", reuse=False): self.actor_tf = self.policy_tf.make_actor(normalized_obs) self.normalized_critic_tf = self.policy_tf.make_critic(normalized_obs, self.actions) self.normalized_critic_with_actor_tf = self.policy_tf.make_critic(normalized_obs, self.actor_tf, reuse=True) # Noise setup if self.param_noise is not None: self._setup_param_noise(normalized_obs) with tf.variable_scope("target", reuse=False): critic_target = self.target_policy.make_critic(normalized_next_obs, self.target_policy.make_actor(normalized_next_obs)) with tf.variable_scope("loss", reuse=False): self.critic_tf = denormalize( tf.clip_by_value(self.normalized_critic_tf, self.return_range[0], self.return_range[1]), self.ret_rms) self.critic_with_actor_tf = denormalize( tf.clip_by_value(self.normalized_critic_with_actor_tf, self.return_range[0], self.return_range[1]), self.ret_rms) q_next_obs = denormalize(critic_target, self.ret_rms) self.target_q = self.rewards + (1. - self.terminals_ph) * self.gamma * q_next_obs tf.summary.scalar('critic_target', tf.reduce_mean(self.critic_target)) if self.full_tensorboard_log: tf.summary.histogram('critic_target', self.critic_target) # Set up parts. if self.normalize_returns and self.enable_popart: self._setup_popart() self._setup_stats() self._setup_target_network_updates() with tf.variable_scope("input_info", reuse=False): tf.summary.scalar('rewards', tf.reduce_mean(self.rewards)) tf.summary.scalar('param_noise_stddev', tf.reduce_mean(self.param_noise_stddev)) if self.full_tensorboard_log: tf.summary.histogram('rewards', self.rewards) tf.summary.histogram('param_noise_stddev', self.param_noise_stddev) if len(self.observation_space.shape) == 3 and self.observation_space.shape[0] in [1, 3, 4]: tf.summary.image('observation', self.obs_train) else: tf.summary.histogram('observation', self.obs_train) with tf.variable_scope("Adam_mpi", reuse=False): self._setup_actor_optimizer() self._setup_critic_optimizer() tf.summary.scalar('actor_loss', self.actor_loss) tf.summary.scalar('critic_loss', self.critic_loss) self.params = tf_util.get_trainable_vars("model") \ + tf_util.get_trainable_vars('noise/') + tf_util.get_trainable_vars('noise_adapt/') self.target_params = tf_util.get_trainable_vars("target") self.obs_rms_params = [var for var in tf.global_variables() if "obs_rms" in] self.ret_rms_params = [var for var in tf.global_variables() if "ret_rms" in] with self.sess.as_default(): self._initialize(self.sess) self.summary = tf.summary.merge_all()
def _setup_target_network_updates(self): """ set the target update operations """ init_updates, soft_updates = get_target_updates(tf_util.get_trainable_vars('model/'), tf_util.get_trainable_vars('target/'), self.tau, self.verbose) self.target_init_updates = init_updates self.target_soft_updates = soft_updates def _setup_param_noise(self, normalized_obs): """ Setup the parameter noise operations :param normalized_obs: (TensorFlow Tensor) the normalized observation """ assert self.param_noise is not None with tf.variable_scope("noise", reuse=False): self.perturbed_actor_tf = self.param_noise_actor.make_actor(normalized_obs) with tf.variable_scope("noise_adapt", reuse=False): adaptive_actor_tf = self.adaptive_param_noise_actor.make_actor(normalized_obs) with tf.variable_scope("noise_update_func", reuse=False): if self.verbose >= 2:'setting up param noise') self.perturb_policy_ops = get_perturbed_actor_updates('model/pi/', 'noise/pi/', self.param_noise_stddev, verbose=self.verbose) self.perturb_adaptive_policy_ops = get_perturbed_actor_updates('model/pi/', 'noise_adapt/pi/', self.param_noise_stddev, verbose=self.verbose) self.adaptive_policy_distance = tf.sqrt(tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf))) def _setup_actor_optimizer(self): """ setup the optimizer for the actor """ if self.verbose >= 2:'setting up actor optimizer') self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf) actor_shapes = [var.get_shape().as_list() for var in tf_util.get_trainable_vars('model/pi/')] actor_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in actor_shapes]) if self.verbose >= 2:' actor shapes: {}'.format(actor_shapes))' actor params: {}'.format(actor_nb_params)) self.actor_grads = tf_util.flatgrad(self.actor_loss, tf_util.get_trainable_vars('model/pi/'), clip_norm=self.clip_norm) self.actor_optimizer = MpiAdam(var_list=tf_util.get_trainable_vars('model/pi/'), beta1=0.9, beta2=0.999, epsilon=1e-08) def _setup_critic_optimizer(self): """ setup the optimizer for the critic """ if self.verbose >= 2:'setting up critic optimizer') normalized_critic_target_tf = tf.clip_by_value(normalize(self.critic_target, self.ret_rms), self.return_range[0], self.return_range[1]) self.critic_loss = tf.reduce_mean(tf.square(self.normalized_critic_tf - normalized_critic_target_tf)) if self.critic_l2_reg > 0.: critic_reg_vars = [var for var in tf_util.get_trainable_vars('model/qf/') if 'bias' not in and 'qf_output' not in and 'b' not in] if self.verbose >= 2: for var in critic_reg_vars:' regularizing: {}'.format(' applying l2 regularization with {}'.format(self.critic_l2_reg)) critic_reg = tc.layers.apply_regularization( tc.layers.l2_regularizer(self.critic_l2_reg), weights_list=critic_reg_vars ) self.critic_loss += critic_reg critic_shapes = [var.get_shape().as_list() for var in tf_util.get_trainable_vars('model/qf/')] critic_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in critic_shapes]) if self.verbose >= 2:' critic shapes: {}'.format(critic_shapes))' critic params: {}'.format(critic_nb_params)) self.critic_grads = tf_util.flatgrad(self.critic_loss, tf_util.get_trainable_vars('model/qf/'), clip_norm=self.clip_norm) self.critic_optimizer = MpiAdam(var_list=tf_util.get_trainable_vars('model/qf/'), beta1=0.9, beta2=0.999, epsilon=1e-08) def _setup_popart(self): """ setup pop-art normalization of the critic output See for details. Preserving Outputs Precisely, while Adaptively Rescaling Targets”. """ self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std') new_std = self.ret_rms.std self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean') new_mean = self.ret_rms.mean self.renormalize_q_outputs_op = [] for out_vars in [[var for var in tf_util.get_trainable_vars('model/qf/') if 'qf_output' in], [var for var in tf_util.get_trainable_vars('target/qf/') if 'qf_output' in]]: assert len(out_vars) == 2 # wieght and bias of the last layer weight, bias = out_vars assert 'kernel' in assert 'bias' in assert weight.get_shape()[-1] == 1 assert bias.get_shape()[-1] == 1 self.renormalize_q_outputs_op += [weight.assign(weight * self.old_std / new_std)] self.renormalize_q_outputs_op += [bias.assign((bias * self.old_std + self.old_mean - new_mean) / new_std)] def _setup_stats(self): """ Setup the stat logger for DDPG. """ ops = [ tf.reduce_mean(self.critic_tf), reduce_std(self.critic_tf), tf.reduce_mean(self.critic_with_actor_tf), reduce_std(self.critic_with_actor_tf), tf.reduce_mean(self.actor_tf), reduce_std(self.actor_tf) ] names = [ 'reference_Q_mean', 'reference_Q_std', 'reference_actor_Q_mean', 'reference_actor_Q_std', 'reference_action_mean', 'reference_action_std' ] if self.normalize_returns: ops += [self.ret_rms.mean, self.ret_rms.std] names += ['ret_rms_mean', 'ret_rms_std'] if self.normalize_observations: ops += [tf.reduce_mean(self.obs_rms.mean), tf.reduce_mean(self.obs_rms.std)] names += ['obs_rms_mean', 'obs_rms_std'] if self.param_noise: ops += [tf.reduce_mean(self.perturbed_actor_tf), reduce_std(self.perturbed_actor_tf)] names += ['reference_perturbed_action_mean', 'reference_perturbed_action_std'] self.stats_ops = ops self.stats_names = names def _policy(self, obs, apply_noise=True, compute_q=True): """ Get the actions and critic output, from a given observation :param obs: ([float] or [int]) the observation :param apply_noise: (bool) enable the noise :param compute_q: (bool) compute the critic output :return: ([float], float) the action and critic value """ obs = np.array(obs).reshape((-1,) + self.observation_space.shape) feed_dict = {self.obs_train: obs} if self.param_noise is not None and apply_noise: actor_tf = self.perturbed_actor_tf feed_dict[self.obs_noise] = obs else: actor_tf = self.actor_tf if compute_q: action, q_value =[actor_tf, self.critic_with_actor_tf], feed_dict=feed_dict) else: action =, feed_dict=feed_dict) q_value = None action = action.flatten() if self.action_noise is not None and apply_noise: noise = self.action_noise() action += noise action = np.clip(action, -1, 1) return action, q_value def _store_transition(self, obs, action, reward, next_obs, done): """ Store a transition in the replay buffer :param obs: ([float] or [int]) the last observation :param action: ([float]) the action :param reward: (float] the reward :param next_obs: ([float] or [int]) the current observation :param done: (bool) Whether the episode is over """ reward *= self.reward_scale self.replay_buffer.add(obs, action, reward, next_obs, float(done)) if self.normalize_observations: self.obs_rms.update(np.array([obs])) def _train_step(self, step, writer, log=False): """ run a step of training from batch :param step: (int) the current step iteration :param writer: (TensorFlow Summary.writer) the writer for tensorboard :param log: (bool) whether or not to log to metadata :return: (float, float) critic loss, actor loss """ # Get a batch obs, actions, rewards, next_obs, terminals = self.replay_buffer.sample(batch_size=self.batch_size) # Reshape to match previous behavior and placeholder shape rewards = rewards.reshape(-1, 1) terminals = terminals.reshape(-1, 1) if self.normalize_returns and self.enable_popart: old_mean, old_std, target_q =[self.ret_rms.mean, self.ret_rms.std, self.target_q], feed_dict={ self.obs_target: next_obs, self.rewards: rewards, self.terminals_ph: terminals }) self.ret_rms.update(target_q.flatten()), feed_dict={ self.old_std: np.array([old_std]), self.old_mean: np.array([old_mean]), }) else: target_q =, feed_dict={ self.obs_target: next_obs, self.rewards: rewards, self.terminals_ph: terminals }) # Get all gradients and perform a synced update. ops = [self.actor_grads, self.actor_loss, self.critic_grads, self.critic_loss] td_map = { self.obs_train: obs, self.actions: actions, self.action_train_ph: actions, self.rewards: rewards, self.critic_target: target_q, self.param_noise_stddev: 0 if self.param_noise is None else self.param_noise.current_stddev } if writer is not None: # run loss backprop with summary if the step_id was not already logged (can happen with the right # parameters as the step value is only an estimate) if self.full_tensorboard_log and log and step not in self.tb_seen_steps: run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) run_metadata = tf.RunMetadata() summary, actor_grads, actor_loss, critic_grads, critic_loss = \[self.summary] + ops, td_map, options=run_options, run_metadata=run_metadata) writer.add_run_metadata(run_metadata, 'step%d' % step) self.tb_seen_steps.append(step) else: summary, actor_grads, actor_loss, critic_grads, critic_loss =[self.summary] + ops, td_map) writer.add_summary(summary, step) else: actor_grads, actor_loss, critic_grads, critic_loss =, td_map) self.actor_optimizer.update(actor_grads, learning_rate=self.actor_lr) self.critic_optimizer.update(critic_grads, learning_rate=self.critic_lr) return critic_loss, actor_loss def _initialize(self, sess): """ initialize the model parameters and optimizers :param sess: (TensorFlow Session) the current TensorFlow session """ self.sess = sess self.actor_optimizer.sync() self.critic_optimizer.sync() def _update_target_net(self): """ run target soft update operation """ def _get_stats(self): """ Get the mean and standard deviation of the model's inputs and outputs :return: (dict) the means and stds """ if self.stats_sample is None: # Get a sample and keep that fixed for all further computations. # This allows us to estimate the change in value for the same set of inputs. obs, actions, rewards, next_obs, terminals = self.replay_buffer.sample(batch_size=self.batch_size) self.stats_sample = { 'obs': obs, 'actions': actions, 'rewards': rewards, 'next_obs': next_obs, 'terminals': terminals } feed_dict = { self.actions: self.stats_sample['actions'] } for placeholder in [self.action_train_ph, self.action_target, self.action_adapt_noise, self.action_noise_ph]: if placeholder is not None: feed_dict[placeholder] = self.stats_sample['actions'] for placeholder in [self.obs_train, self.obs_target, self.obs_adapt_noise, self.obs_noise]: if placeholder is not None: feed_dict[placeholder] = self.stats_sample['obs'] values =, feed_dict=feed_dict) names = self.stats_names[:] assert len(names) == len(values) stats = dict(zip(names, values)) if self.param_noise is not None: stats = {**stats, **self.param_noise.get_stats()} return stats def _adapt_param_noise(self): """ calculate the adaptation for the parameter noise :return: (float) the mean distance for the parameter noise """ if self.param_noise is None: return 0. # Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation. obs, *_ = self.replay_buffer.sample(batch_size=self.batch_size), feed_dict={ self.param_noise_stddev: self.param_noise.current_stddev, }) distance =, feed_dict={ self.obs_adapt_noise: obs, self.obs_train: obs, self.param_noise_stddev: self.param_noise.current_stddev, }) mean_distance = MPI.COMM_WORLD.allreduce(distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size() self.param_noise.adapt(mean_distance) return mean_distance def _reset(self): """ Reset internal state after an episode is complete. """ if self.action_noise is not None: self.action_noise.reset() if self.param_noise is not None:, feed_dict={ self.param_noise_stddev: self.param_noise.current_stddev, })
[docs] def learn(self, total_timesteps, callback=None, log_interval=100, tb_log_name="DDPG", 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() # a list for tensorboard logging, to prevent logging with the same step number, if it already occured self.tb_seen_steps = [] rank = MPI.COMM_WORLD.Get_rank() if self.verbose >= 2: logger.log('Using agent with the following configuration:') logger.log(str(self.__dict__.items())) eval_episode_rewards_history = deque(maxlen=100) episode_rewards_history = deque(maxlen=100) episode_successes = [] with self.sess.as_default(), self.graph.as_default(): # Prepare everything. self._reset() obs = self.env.reset() eval_obs = None if self.eval_env is not None: eval_obs = self.eval_env.reset() episode_reward = 0. episode_step = 0 episodes = 0 step = 0 total_steps = 0 start_time = time.time() epoch_episode_rewards = [] epoch_episode_steps = [] epoch_actor_losses = [] epoch_critic_losses = [] epoch_adaptive_distances = [] eval_episode_rewards = [] eval_qs = [] epoch_actions = [] epoch_qs = [] epoch_episodes = 0 epoch = 0 callback.on_training_start(locals(), globals()) while True: for _ in range(log_interval): callback.on_rollout_start() # Perform rollouts. for _ in range(self.nb_rollout_steps): if total_steps >= total_timesteps: callback.on_training_end() return self # Predict next action. action, q_value = self._policy(obs, apply_noise=True, compute_q=True) assert action.shape == self.env.action_space.shape # Execute next action. if rank == 0 and self.render: self.env.render() # Randomly sample actions from a uniform distribution # with a probability self.random_exploration (used in HER + DDPG) if 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.action_space.sample() action = scale_action(self.action_space, unscaled_action) else: # inferred actions need to be transformed to environment action_space before stepping unscaled_action = unscale_action(self.action_space, action) new_obs, reward, done, info = self.env.step(unscaled_action) self.num_timesteps += 1 if callback.on_step() is False: callback.on_training_end() return self if writer is not None: ep_rew = np.array([reward]).reshape((1, -1)) ep_done = np.array([done]).reshape((1, -1)) tf_util.total_episode_reward_logger(self.episode_reward, ep_rew, ep_done, writer, self.num_timesteps) step += 1 total_steps += 1 if rank == 0 and self.render: self.env.render() episode_reward += reward episode_step += 1 # Book-keeping. epoch_actions.append(action) epoch_qs.append(q_value) self._store_transition(obs, action, reward, new_obs, done) obs = new_obs if done: # Episode done. epoch_episode_rewards.append(episode_reward) episode_rewards_history.append(episode_reward) epoch_episode_steps.append(episode_step) episode_reward = 0. episode_step = 0 epoch_episodes += 1 episodes += 1 maybe_is_success = info.get('is_success') if maybe_is_success is not None: episode_successes.append(float(maybe_is_success)) self._reset() if not isinstance(self.env, VecEnv): obs = self.env.reset() callback.on_rollout_end() # Train. epoch_actor_losses = [] epoch_critic_losses = [] epoch_adaptive_distances = [] for t_train in range(self.nb_train_steps): # Not enough samples in the replay buffer if not self.replay_buffer.can_sample(self.batch_size): break # Adapt param noise, if necessary. if len(self.replay_buffer) >= self.batch_size and \ t_train % self.param_noise_adaption_interval == 0: distance = self._adapt_param_noise() epoch_adaptive_distances.append(distance) # weird equation to deal with the fact the nb_train_steps will be different # to nb_rollout_steps step = (int(t_train * (self.nb_rollout_steps / self.nb_train_steps)) + self.num_timesteps - self.nb_rollout_steps) critic_loss, actor_loss = self._train_step(step, writer, log=t_train == 0) epoch_critic_losses.append(critic_loss) epoch_actor_losses.append(actor_loss) self._update_target_net() # Evaluate. eval_episode_rewards = [] eval_qs = [] if self.eval_env is not None: eval_episode_reward = 0. for _ in range(self.nb_eval_steps): if total_steps >= total_timesteps: return self eval_action, eval_q = self._policy(eval_obs, apply_noise=False, compute_q=True) unscaled_action = unscale_action(self.action_space, eval_action) eval_obs, eval_r, eval_done, _ = self.eval_env.step(unscaled_action) if self.render_eval: self.eval_env.render() eval_episode_reward += eval_r eval_qs.append(eval_q) if eval_done: if not isinstance(self.env, VecEnv): eval_obs = self.eval_env.reset() eval_episode_rewards.append(eval_episode_reward) eval_episode_rewards_history.append(eval_episode_reward) eval_episode_reward = 0. mpi_size = MPI.COMM_WORLD.Get_size() # Log stats. # XXX shouldn't call np.mean on variable length lists duration = time.time() - start_time stats = self._get_stats() combined_stats = stats.copy() combined_stats['rollout/return'] = np.mean(epoch_episode_rewards) combined_stats['rollout/return_history'] = np.mean(episode_rewards_history) combined_stats['rollout/episode_steps'] = np.mean(epoch_episode_steps) combined_stats['rollout/actions_mean'] = np.mean(epoch_actions) combined_stats['rollout/Q_mean'] = np.mean(epoch_qs) combined_stats['train/loss_actor'] = np.mean(epoch_actor_losses) combined_stats['train/loss_critic'] = np.mean(epoch_critic_losses) if len(epoch_adaptive_distances) != 0: combined_stats['train/param_noise_distance'] = np.mean(epoch_adaptive_distances) combined_stats['total/duration'] = duration combined_stats['total/steps_per_second'] = float(step) / float(duration) combined_stats['total/episodes'] = episodes combined_stats['rollout/episodes'] = epoch_episodes combined_stats['rollout/actions_std'] = np.std(epoch_actions) # Evaluation statistics. if self.eval_env is not None: combined_stats['eval/return'] = np.mean(eval_episode_rewards) combined_stats['eval/return_history'] = np.mean(eval_episode_rewards_history) combined_stats['eval/Q'] = np.mean(eval_qs) combined_stats['eval/episodes'] = len(eval_episode_rewards) def as_scalar(scalar): """ check and return the input if it is a scalar, otherwise raise ValueError :param scalar: (Any) the object to check :return: (Number) the scalar if x is a scalar """ if isinstance(scalar, np.ndarray): assert scalar.size == 1 return scalar[0] elif np.isscalar(scalar): return scalar else: raise ValueError('expected scalar, got %s' % scalar) combined_stats_sums = MPI.COMM_WORLD.allreduce( np.array([as_scalar(x) for x in combined_stats.values()])) combined_stats = {k: v / mpi_size for (k, v) in zip(combined_stats.keys(), combined_stats_sums)} # Total statistics. combined_stats['total/epochs'] = epoch + 1 combined_stats['total/steps'] = step for key in sorted(combined_stats.keys()): logger.record_tabular(key, combined_stats[key]) if len(episode_successes) > 0: logger.logkv("success rate", np.mean(episode_successes[-100:])) logger.dump_tabular()'') logdir = logger.get_dir() if rank == 0 and logdir: if hasattr(self.env, 'get_state'): with open(os.path.join(logdir, 'env_state.pkl'), 'wb') as file_handler: pickle.dump(self.env.get_state(), file_handler) if self.eval_env and hasattr(self.eval_env, 'get_state'): with open(os.path.join(logdir, 'eval_env_state.pkl'), 'wb') as file_handler: pickle.dump(self.eval_env.get_state(), file_handler)
[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(observation, apply_noise=not deterministic, compute_q=False) 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 action_probability(self, observation, state=None, mask=None, actions=None, logp=False): _ = np.array(observation) if actions is not None: raise ValueError("Error: DDPG 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 DDPG. Returning None") return None
[docs] def get_parameter_list(self): return (self.params + self.target_params + self.obs_rms_params + self.ret_rms_params)
[docs] def save(self, save_path, cloudpickle=False): data = { "observation_space": self.observation_space, "action_space": self.action_space, "nb_eval_steps": self.nb_eval_steps, "param_noise_adaption_interval": self.param_noise_adaption_interval, "nb_train_steps": self.nb_train_steps, "nb_rollout_steps": self.nb_rollout_steps, "verbose": self.verbose, "param_noise": self.param_noise, "action_noise": self.action_noise, "gamma": self.gamma, "tau": self.tau, "normalize_returns": self.normalize_returns, "enable_popart": self.enable_popart, "normalize_observations": self.normalize_observations, "batch_size": self.batch_size, "observation_range": self.observation_range, "return_range": self.return_range, "critic_l2_reg": self.critic_l2_reg, "actor_lr": self.actor_lr, "critic_lr": self.critic_lr, "clip_norm": self.clip_norm, "reward_scale": self.reward_scale, "memory_limit": self.memory_limit, "buffer_size": self.buffer_size, "random_exploration": self.random_exploration, "policy": self.policy, "n_envs": self.n_envs, "n_cpu_tf_sess": self.n_cpu_tf_sess, "seed": self.seed, "_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)
[docs] @classmethod def load(cls, load_path, env=None, custom_objects=None, **kwargs): data, params = cls._load_from_file(load_path, custom_objects=custom_objects) if 'policy_kwargs' in kwargs and kwargs['policy_kwargs'] != data['policy_kwargs']: raise ValueError("The specified policy kwargs do not equal the stored policy kwargs. " "Stored kwargs: {}, specified kwargs: {}".format(data['policy_kwargs'], kwargs['policy_kwargs'])) model = cls(None, env, _init_setup_model=False) model.__dict__.update(data) model.__dict__.update(kwargs) model.set_env(env) model.setup_model() # Patch for version < v2.6.0, duplicated keys where saved if len(params) > len(model.get_parameter_list()): n_params = len(model.params) n_target_params = len(model.target_params) n_normalisation_params = len(model.obs_rms_params) + len(model.ret_rms_params) # Check that the issue is the one from # assert len(params) == 2 * (n_params + n_target_params) + n_normalisation_params,\ "The number of parameter saved differs from the number of parameters"\ " that should be loaded: {}!={}".format(len(params), len(model.get_parameter_list())) # Remove duplicates params_ = params[:n_params + n_target_params] if n_normalisation_params > 0: params_ += params[-n_normalisation_params:] params = params_ model.load_parameters(params) return model