Parameter space noise for DQN and DDPG (#75)

* Export param noise

* Update documentation

* Final finishing touches

README.md

@@ -15,3 +15,4 @@ pip install baselines
 - [DQN](baselines/deepq)
 - [PPO](baselines/pposgd)
 - [TRPO](baselines/trpo_mpi)
+- [DDPG](baselines/ddpg)

baselines/common/azure_utils.py

@@ -3,7 +3,10 @@ import tempfile
 import zipfile
 
 from azure.common import AzureMissingResourceHttpError
-from azure.storage.blob import BlobService
+try:
+    from azure.storage.blob import BlobService
+except ImportError:
+    from azure.storage.blob import BlockBlobService as BlobService
 from shutil import unpack_archive
 from threading import Event
 
@@ -114,18 +117,23 @@ class Container(object):
             arcpath = os.path.join(td, "archive.zip")
             for backup_blob_name in [blob_name, blob_name + '.backup']:
                 try:
-                    blob_size = self._service.get_blob_properties(
+                    properties = self._service.get_blob_properties(
                         blob_name=backup_blob_name,
                         container_name=self._container_name
-                    )['content-length']
+                    )
+                    if hasattr(properties, 'properties'):
+                        # Annoyingly, Azure has changed the API and this now returns a blob
+                        # instead of it's properties with up-to-date azure package.
+                        blob_size = properties.properties.content_length
+                    else:
+                        blob_size = properties['content-length']
                     if int(blob_size) > 0:
                         self._service.get_blob_to_path(
                             container_name=self._container_name,
                             blob_name=backup_blob_name,
                             file_path=arcpath,
                             max_connections=4,
-                            progress_callback=progress_callback,
-                            max_retries=10)
+                            progress_callback=progress_callback)
                         unpack_archive(arcpath, dest_path)
                         download_done.wait()
                         return True

baselines/common/misc_util.py

@@ -237,8 +237,9 @@ def boolean_flag(parser, name, default=False, help=None):
     help: str
         help string for the flag
     """
-    parser.add_argument("--" + name, action="store_true", default=default, help=help)
-    parser.add_argument("--no-" + name, action="store_false", dest=name)
+    dest = name.replace('-', '_')
+    parser.add_argument("--" + name, action="store_true", default=default, dest=dest, help=help)
+    parser.add_argument("--no-" + name, action="store_false", dest=dest)
 
 
 def get_wrapper_by_name(env, classname):

baselines/common/mpi_fork.py

 import os, subprocess, sys
 
-def mpi_fork(n):
+def mpi_fork(n, bind_to_core=False):
     """Re-launches the current script with workers
     Returns "parent" for original parent, "child" for MPI children
     """
@@ -13,7 +13,11 @@ def mpi_fork(n):
             OMP_NUM_THREADS="1",
             IN_MPI="1"
         )
-        subprocess.check_call(["mpirun", "-np", str(n), sys.executable] + sys.argv, env=env)
+        args = ["mpirun", "-np", str(n)]
+        if bind_to_core:
+            args += ["-bind-to", "core"]
+        args += [sys.executable] + sys.argv
+        subprocess.check_call(args, env=env)
         return "parent"
     else:
         return "child"

baselines/ddpg/ddpg.py

+from copy import copy
+from functools import reduce
+
+import numpy as np
+import tensorflow as tf
+import tensorflow.contrib as tc
+
+from baselines import logger
+from baselines.common.mpi_adam import MpiAdam
+import baselines.common.tf_util as U
+from baselines.common.mpi_running_mean_std import RunningMeanStd
+from baselines.ddpg.util import reduce_std, mpi_mean
+
+
+def normalize(x, stats):
+    if stats is None:
+        return x
+    return (x - stats.mean) / stats.std
+
+
+def denormalize(x, stats):
+    if stats is None:
+        return x
+    return x * stats.std + stats.mean
+
+
+def get_target_updates(vars, target_vars, tau):
+    logger.info('setting up target updates ...')
+    soft_updates = []
+    init_updates = []
+    assert len(vars) == len(target_vars)
+    for var, target_var in zip(vars, target_vars):
+        logger.info('  {} <- {}'.format(target_var.name, var.name))
+        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)
+    return tf.group(*init_updates), tf.group(*soft_updates)
+
+
+def get_perturbed_actor_updates(actor, perturbed_actor, param_noise_stddev):
+    assert len(actor.vars) == len(perturbed_actor.vars)
+    assert len(actor.perturbable_vars) == len(perturbed_actor.perturbable_vars)
+
+    updates = []
+    for var, perturbed_var in zip(actor.vars, perturbed_actor.vars):
+        if var in actor.perturbable_vars:
+            logger.info('  {} <- {} + noise'.format(perturbed_var.name, var.name))
+            updates.append(tf.assign(perturbed_var, var + tf.random_normal(tf.shape(var), mean=0., stddev=param_noise_stddev)))
+        else:
+            logger.info('  {} <- {}'.format(perturbed_var.name, var.name))
+            updates.append(tf.assign(perturbed_var, var))
+    assert len(updates) == len(actor.vars)
+    return tf.group(*updates)
+
+
+class DDPG(object):
+    def __init__(self, actor, critic, memory, observation_shape, action_shape, param_noise=None, action_noise=None,
+        gamma=0.99, tau=0.001, normalize_returns=False, enable_popart=False, normalize_observations=True,
+        batch_size=128, observation_range=(-5., 5.), action_range=(-1., 1.), return_range=(-np.inf, np.inf),
+        adaptive_param_noise=True, adaptive_param_noise_policy_threshold=.1,
+        critic_l2_reg=0., actor_lr=1e-4, critic_lr=1e-3, clip_norm=None, reward_scale=1.):
+        # Inputs.
+        self.obs0 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs0')
+        self.obs1 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs1')
+        self.terminals1 = tf.placeholder(tf.float32, shape=(None, 1), name='terminals1')
+        self.rewards = tf.placeholder(tf.float32, shape=(None, 1), name='rewards')
+        self.actions = tf.placeholder(tf.float32, shape=(None,) + action_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')
+
+        # Parameters.
+        self.gamma = gamma
+        self.tau = tau
+        self.memory = memory
+        self.normalize_observations = normalize_observations
+        self.normalize_returns = normalize_returns
+        self.action_noise = action_noise
+        self.param_noise = param_noise
+        self.action_range = action_range
+        self.return_range = return_range
+        self.observation_range = observation_range
+        self.critic = critic
+        self.actor = actor
+        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.stats_sample = None
+        self.critic_l2_reg = critic_l2_reg
+
+        # Observation normalization.
+        if self.normalize_observations:
+            with tf.variable_scope('obs_rms'):
+                self.obs_rms = RunningMeanStd(shape=observation_shape)
+        else:
+            self.obs_rms = None
+        normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
+            self.observation_range[0], self.observation_range[1])
+        normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
+            self.observation_range[0], self.observation_range[1])
+
+        # Return normalization.
+        if self.normalize_returns:
+            with tf.variable_scope('ret_rms'):
+                self.ret_rms = RunningMeanStd()
+        else:
+            self.ret_rms = None
+
+        # Create target networks.
+        target_actor = copy(actor)
+        target_actor.name = 'target_actor'
+        self.target_actor = target_actor
+        target_critic = copy(critic)
+        target_critic.name = 'target_critic'
+        self.target_critic = target_critic
+
+        # Create networks and core TF parts that are shared across setup parts.
+        self.actor_tf = actor(normalized_obs0)
+        self.normalized_critic_tf = critic(normalized_obs0, self.actions)
+        self.critic_tf = denormalize(tf.clip_by_value(self.normalized_critic_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
+        self.normalized_critic_with_actor_tf = critic(normalized_obs0, self.actor_tf, reuse=True)
+        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_obs1 = denormalize(target_critic(normalized_obs1, target_actor(normalized_obs1)), self.ret_rms)
+        self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1
+
+        # Set up parts.
+        if self.param_noise is not None:
+            self.setup_param_noise(normalized_obs0)
+        self.setup_actor_optimizer()
+        self.setup_critic_optimizer()
+        if self.normalize_returns and self.enable_popart:
+            self.setup_popart()
+        self.setup_stats()
+        self.setup_target_network_updates()
+
+    def setup_target_network_updates(self):
+        actor_init_updates, actor_soft_updates = get_target_updates(self.actor.vars, self.target_actor.vars, self.tau)
+        critic_init_updates, critic_soft_updates = get_target_updates(self.critic.vars, self.target_critic.vars, self.tau)
+        self.target_init_updates = [actor_init_updates, critic_init_updates]
+        self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
+
+    def setup_param_noise(self, normalized_obs0):
+        assert self.param_noise is not None
+
+        # Configure perturbed actor.
+        param_noise_actor = copy(self.actor)
+        param_noise_actor.name = 'param_noise_actor'
+        self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
+        logger.info('setting up param noise')
+        self.perturb_policy_ops = get_perturbed_actor_updates(self.actor, param_noise_actor, self.param_noise_stddev)
+
+        # Configure separate copy for stddev adoption.
+        adaptive_param_noise_actor = copy(self.actor)
+        adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
+        adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
+        self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
+        self.adaptive_policy_distance = tf.sqrt(tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
+
+    def setup_actor_optimizer(self):
+        logger.info('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 self.actor.trainable_vars]
+        actor_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
+        logger.info('  actor shapes: {}'.format(actor_shapes))
+        logger.info('  actor params: {}'.format(actor_nb_params))
+        self.actor_grads = U.flatgrad(self.actor_loss, self.actor.trainable_vars, clip_norm=self.clip_norm)
+        self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
+            beta1=0.9, beta2=0.999, epsilon=1e-08)
+
+    def setup_critic_optimizer(self):
+        logger.info('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 self.critic.trainable_vars if 'kernel' in var.name and 'output' not in var.name]
+            for var in critic_reg_vars:
+                logger.info('  regularizing: {}'.format(var.name))
+            logger.info('  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 self.critic.trainable_vars]
+        critic_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
+        logger.info('  critic shapes: {}'.format(critic_shapes))
+        logger.info('  critic params: {}'.format(critic_nb_params))
+        self.critic_grads = U.flatgrad(self.critic_loss, self.critic.trainable_vars, clip_norm=self.clip_norm)
+        self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
+            beta1=0.9, beta2=0.999, epsilon=1e-08)
+
+    def setup_popart(self):
+        # See https://arxiv.org/pdf/1602.07714.pdf for details.
+        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 vs in [self.critic.output_vars, self.target_critic.output_vars]:
+            assert len(vs) == 2
+            M, b = vs
+            assert 'kernel' in M.name
+            assert 'bias' in b.name
+            assert M.get_shape()[-1] == 1
+            assert b.get_shape()[-1] == 1
+            self.renormalize_Q_outputs_op += [M.assign(M * self.old_std / new_std)]
+            self.renormalize_Q_outputs_op += [b.assign((b * self.old_std + self.old_mean - new_mean) / new_std)]
+
+    def setup_stats(self):
+        ops = []
+        names = []
+        
+        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']
+        
+        ops += [tf.reduce_mean(self.critic_tf)]
+        names += ['reference_Q_mean']
+        ops += [reduce_std(self.critic_tf)]
+        names += ['reference_Q_std']
+
+        ops += [tf.reduce_mean(self.critic_with_actor_tf)]
+        names += ['reference_actor_Q_mean']
+        ops += [reduce_std(self.critic_with_actor_tf)]
+        names += ['reference_actor_Q_std']
+        
+        ops += [tf.reduce_mean(self.actor_tf)]
+        names += ['reference_action_mean']
+        ops += [reduce_std(self.actor_tf)]
+        names += ['reference_action_std']
+
+        if self.param_noise:
+            ops += [tf.reduce_mean(self.perturbed_actor_tf)]
+            names += ['reference_perturbed_action_mean']
+            ops += [reduce_std(self.perturbed_actor_tf)]
+            names += ['reference_perturbed_action_std']
+
+        self.stats_ops = ops
+        self.stats_names = names
+
+    def pi(self, obs, apply_noise=True, compute_Q=True):
+        if self.param_noise is not None and apply_noise:
+            actor_tf = self.perturbed_actor_tf
+        else:
+            actor_tf = self.actor_tf
+        feed_dict = {self.obs0: [obs]}
+        if compute_Q:
+            action, q = self.sess.run([actor_tf, self.critic_with_actor_tf], feed_dict=feed_dict)
+        else:
+            action = self.sess.run(actor_tf, feed_dict=feed_dict)
+            q = None
+        action = action.flatten()
+        if self.action_noise is not None and apply_noise:
+            noise = self.action_noise()
+            assert noise.shape == action.shape
+            action += noise
+        action = np.clip(action, self.action_range[0], self.action_range[1])
+        return action, q
+
+    def store_transition(self, obs0, action, reward, obs1, terminal1):
+        reward *= self.reward_scale
+        self.memory.append(obs0, action, reward, obs1, terminal1)
+        if self.normalize_observations:
+            self.obs_rms.update(np.array([obs0]))
+
+    def train(self):
+        # Get a batch.
+        batch = self.memory.sample(batch_size=self.batch_size)
+
+        if self.normalize_returns and self.enable_popart:
+            old_mean, old_std, target_Q = self.sess.run([self.ret_rms.mean, self.ret_rms.std, self.target_Q], feed_dict={
+                self.obs1: batch['obs1'],
+                self.rewards: batch['rewards'],
+                self.terminals1: batch['terminals1'].astype('float32'),
+            })
+            self.ret_rms.update(target_Q.flatten())
+            self.sess.run(self.renormalize_Q_outputs_op, feed_dict={
+                self.old_std : np.array([old_std]),
+                self.old_mean : np.array([old_mean]),
+            })
+
+            # Run sanity check. Disabled by default since it slows down things considerably.
+            # print('running sanity check')
+            # target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
+            #     self.obs1: batch['obs1'],
+            #     self.rewards: batch['rewards'],
+            #     self.terminals1: batch['terminals1'].astype('float32'),
+            # })
+            # print(target_Q_new, target_Q, new_mean, new_std)
+            # assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
+        else:
+            target_Q = self.sess.run(self.target_Q, feed_dict={
+                self.obs1: batch['obs1'],
+                self.rewards: batch['rewards'],
+                self.terminals1: batch['terminals1'].astype('float32'),
+            })
+
+        # Get all gradients and perform a synced update.
+        ops = [self.actor_grads, self.actor_loss, self.critic_grads, self.critic_loss]
+        actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(ops, feed_dict={
+            self.obs0: batch['obs0'],
+            self.actions: batch['actions'],
+            self.critic_target: target_Q,
+        })
+        self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
+        self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
+
+        return critic_loss, actor_loss
+
+    def initialize(self, sess):
+        self.sess = sess
+        self.sess.run(tf.global_variables_initializer())
+        self.actor_optimizer.sync()
+        self.critic_optimizer.sync()
+        self.sess.run(self.target_init_updates)
+
+    def update_target_net(self):
+        self.sess.run(self.target_soft_updates)
+
+    def get_stats(self):
+        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.
+            self.stats_sample = self.memory.sample(batch_size=self.batch_size)
+        values = self.sess.run(self.stats_ops, feed_dict={
+            self.obs0: self.stats_sample['obs0'],
+            self.actions: self.stats_sample['actions'],
+        })
+
+        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):
+        if self.param_noise is None:
+            return 0.
+        
+        # Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
+        batch = self.memory.sample(batch_size=self.batch_size)
+        self.sess.run(self.perturb_adaptive_policy_ops, feed_dict={
+            self.param_noise_stddev: self.param_noise.current_stddev,
+        })
+        distance = self.sess.run(self.adaptive_policy_distance, feed_dict={
+            self.obs0: batch['obs0'],
+            self.param_noise_stddev: self.param_noise.current_stddev,
+        })
+
+        mean_distance = mpi_mean(distance)
+        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:
+            self.sess.run(self.perturb_policy_ops, feed_dict={
+                self.param_noise_stddev: self.param_noise.current_stddev,
+            })

baselines/ddpg/main.py

+import argparse
+import time
+import os
+from tempfile import mkdtemp
+import sys
+import subprocess
+import threading
+import json
+
+from baselines.common.mpi_fork import mpi_fork
+from baselines import logger
+from baselines.logger import Logger
+from baselines.common.misc_util import (
+    set_global_seeds,
+    boolean_flag,
+    SimpleMonitor
+)
+import baselines.ddpg.training as training
+from baselines.ddpg.models import Actor, Critic
+from baselines.ddpg.memory import Memory
+from baselines.ddpg.noise import *
+
+import gym
+import tensorflow as tf
+from mpi4py import MPI
+
+
+def run(env_id, seed, noise_type, num_cpu, layer_norm, logdir, gym_monitor, evaluation, bind_to_core, **kwargs):
+    kwargs['logdir'] = logdir
+    whoami = mpi_fork(num_cpu, bind_to_core=bind_to_core)
+    if whoami == 'parent':
+        sys.exit(0)
+
+    # Configure things.
+    rank = MPI.COMM_WORLD.Get_rank()
+    if rank != 0:
+        # Write to temp directory for all non-master workers.
+        actual_dir = None
+        Logger.CURRENT.close()
+        Logger.CURRENT = Logger(dir=mkdtemp(), output_formats=[])
+        logger.set_level(logger.DISABLED)
+    
+    # Create envs.
+    if rank == 0:
+        env = gym.make(env_id)
+        if gym_monitor and logdir:
+            env = gym.wrappers.Monitor(env, os.path.join(logdir, 'gym_train'), force=True)
+        env = SimpleMonitor(env)
+
+        if evaluation:
+            eval_env = gym.make(env_id)
+            if gym_monitor and logdir:
+                eval_env = gym.wrappers.Monitor(eval_env, os.path.join(logdir, 'gym_eval'), force=True)
+            eval_env = SimpleMonitor(eval_env)
+        else:
+            eval_env = None
+    else:
+        env = gym.make(env_id)
+        if evaluation:
+            eval_env = gym.make(env_id)
+        else:
+            eval_env = None
+
+    # Parse noise_type
+    action_noise = None
+    param_noise = None
+    nb_actions = env.action_space.shape[-1]
+    for current_noise_type in noise_type.split(','):
+        current_noise_type = current_noise_type.strip()
+        if current_noise_type == 'none':
+            pass
+        elif 'adaptive-param' in current_noise_type:
+            _, stddev = current_noise_type.split('_')
+            param_noise = AdaptiveParamNoiseSpec(initial_stddev=float(stddev), desired_action_stddev=float(stddev))
+        elif 'normal' in current_noise_type:
+            _, stddev = current_noise_type.split('_')
+            action_noise = NormalActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
+        elif 'ou' in current_noise_type:
+            _, stddev = current_noise_type.split('_')
+            action_noise = OrnsteinUhlenbeckActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
+        else:
+            raise RuntimeError('unknown noise type "{}"'.format(current_noise_type))
+
+    # Configure components.
+    memory = Memory(limit=int(1e6), action_shape=env.action_space.shape, observation_shape=env.observation_space.shape)
+    critic = Critic(layer_norm=layer_norm)
+    actor = Actor(nb_actions, layer_norm=layer_norm)
+
+    # Seed everything to make things reproducible.
+    seed = seed + 1000000 * rank
+    logger.info('rank {}: seed={}, logdir={}'.format(rank, seed, logger.get_dir()))
+    tf.reset_default_graph()
+    set_global_seeds(seed)
+    env.seed(seed)
+    if eval_env is not None:
+        eval_env.seed(seed)
+
+    # Disable logging for rank != 0 to avoid noise.
+    if rank == 0:
+        start_time = time.time()
+    training.train(env=env, eval_env=eval_env, param_noise=param_noise,
+        action_noise=action_noise, actor=actor, critic=critic, memory=memory, **kwargs)
+    env.close()
+    if eval_env is not None:
+        eval_env.close()
+    Logger.CURRENT.close()
+    if rank == 0:
+        logger.info('total runtime: {}s'.format(time.time() - start_time))
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    
+    parser.add_argument('--env-id', type=str, default='HalfCheetah-v1')
+    boolean_flag(parser, 'render-eval', default=False)
+    boolean_flag(parser, 'layer-norm', default=True)
+    boolean_flag(parser, 'render', default=False)
+    parser.add_argument('--num-cpu', type=int, default=1)
+    boolean_flag(parser, 'normalize-returns', default=False)
+    boolean_flag(parser, 'normalize-observations', default=True)
+    parser.add_argument('--seed', type=int, default=0)
+    parser.add_argument('--critic-l2-reg', type=float, default=1e-2)
+    parser.add_argument('--batch-size', type=int, default=64)  # per MPI worker
+    parser.add_argument('--actor-lr', type=float, default=1e-4)
+    parser.add_argument('--critic-lr', type=float, default=1e-3)
+    boolean_flag(parser, 'popart', default=False)
+    parser.add_argument('--gamma', type=float, default=0.99)
+    parser.add_argument('--reward-scale', type=float, default=1.)
+    parser.add_argument('--clip-norm', type=float, default=None)
+    parser.add_argument('--nb-epochs', type=int, default=500)  # with default settings, perform 1M steps total
+    parser.add_argument('--nb-epoch-cycles', type=int,<