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#!/usr/bin/env python
import argparse
import bullet_cartpole
import collections
import datetime
import gym
import json
import numpy as np
import replay_memory
import signal
import sys
import tensorflow as tf
import time
import util
np.set_printoptions(precision=5, threshold=10000, suppress=True, linewidth=10000)
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--num-eval', type=int, default=0,
help="if >0 just run this many episodes with no training")
parser.add_argument('--max-num-actions', type=int, default=0,
help="train for (at least) this number of actions (always finish current episode)"
" ignore if <=0")
parser.add_argument('--max-run-time', type=int, default=0,
help="train for (at least) this number of seconds (always finish current episode)"
" ignore if <=0")
parser.add_argument('--ckpt-dir', type=str, default=None, help="if set save ckpts to this dir")
parser.add_argument('--ckpt-freq', type=int, default=3600, help="freq (sec) to save ckpts")
parser.add_argument('--batch-size', type=int, default=128, help="training batch size")
parser.add_argument('--batches-per-step', type=int, default=5,
help="number of batches to train per step")
parser.add_argument('--dont-do-rollouts', action="store_true",
help="by dft we do rollouts to generate data then train after each rollout. if this flag is set we"
" dont do any rollouts. this only makes sense to do if --event-log-in set.")
parser.add_argument('--target-update-rate', type=float, default=0.0001,
help="affine combo for updating target networks each time we run a training batch")
parser.add_argument('--use-batch-norm', action='store_true',
help="whether to use batch norm on conv layers")
parser.add_argument('--actor-hidden-layers', type=str, default="100,100,50", help="actor hidden layer sizes")
parser.add_argument('--critic-hidden-layers', type=str, default="100,100,50", help="critic hidden layer sizes")
parser.add_argument('--actor-learning-rate', type=float, default=0.001, help="learning rate for actor")
parser.add_argument('--critic-learning-rate', type=float, default=0.01, help="learning rate for critic")
parser.add_argument('--discount', type=float, default=0.99, help="discount for RHS of critic bellman equation update")
parser.add_argument('--event-log-in', type=str, default=None,
help="prepopulate replay memory with entries from this event log")
parser.add_argument('--replay-memory-size', type=int, default=22000, help="max size of replay memory")
parser.add_argument('--replay-memory-burn-in', type=int, default=1000, help="dont train from replay memory until it reaches this size")
parser.add_argument('--eval-action-noise', action='store_true', help="whether to use noise during eval")
parser.add_argument('--action-noise-theta', type=float, default=0.01,
help="OrnsteinUhlenbeckNoise theta (rate of change) param for action exploration")
parser.add_argument('--action-noise-sigma', type=float, default=0.05,
help="OrnsteinUhlenbeckNoise sigma (magnitude) param for action exploration")
opts = parser.parse_args()
sys.stderr.write("%s\n" % opts)
# TODO: if we import slim _before_ building cartpole env we can't start bullet with GL gui o_O
env = bullet_cartpole.BulletCartpole(opts=opts, discrete_actions=False)
import base_network
import tensorflow.contrib.slim as slim
def toggle_verbose_debug(signal, frame):
signal.signal(signal.SIGUSR1, toggle_verbose_debug)
def set_dump_weights(signal, frame):
signal.signal(signal.SIGUSR2, set_dump_weights)
class ActorNetwork(base_network.Network):
""" the actor represents the learnt policy mapping states to actions"""
def __init__(self, namespace, input_state, action_dim):
super(ActorNetwork, self).__init__(namespace)
self.input_state = input_state
self.exploration_noise = util.OrnsteinUhlenbeckNoise(action_dim,
with tf.variable_scope(namespace):
opts.hidden_layers = opts.actor_hidden_layers
final_hidden = self.input_state_network(self.input_state, opts)
# action dim output. note: actors out is (-1, 1) and scaled in env as required.
weights_initializer = tf.random_uniform_initializer(-0.001, 0.001)
self.output_action = slim.fully_connected(scope='output_action',
def init_ops_for_training(self, critic):
# actors gradients are the gradients for it's output w.r.t it's vars using initial
# gradients provided by critic. this requires that critic was init'd with an
# input_action = actor.output_action (which is natural anyway)
# we wrap the optimiser in namespace since we don't want this as part of copy to
# target networks.
# note that we negate the gradients from critic since we are trying to maximise
# the q values (not minimise like a loss)
with tf.variable_scope("optimiser"):
gradients = tf.gradients(self.output_action,
gradients = zip(gradients, self.trainable_model_vars())
# potentially clip and wrap with debugging
gradients = util.clip_and_debug_gradients(gradients, opts)
# apply
optimiser = tf.train.GradientDescentOptimizer(opts.actor_learning_rate)
self.train_op = optimiser.apply_gradients(gradients)
def action_given(self, state, add_noise=False):
# feed explicitly provided state
actions = tf.get_default_session().run(self.output_action,
feed_dict={self.input_state: [state],
base_network.IS_TRAINING: False})
# NOTE: noise is added _outside_ tf graph. we do this simply because the noisy output
# is never used for any part of computation graph required for online training. it's
# only used during training after being the replay buffer.
if add_noise:
pre_noise = str(actions)
actions[0] += self.exploration_noise.sample()
actions = np.clip(1, -1, actions) # action output is _always_ (-1, 1)
print "TRAIN action_given pre_noise %s post_noise %s" % (pre_noise, actions)
return actions
def train(self, state):
# training actor only requires state since we are trying to maximise the
# q_value according to the critic.
feed_dict={self.input_state: state,
base_network.IS_TRAINING: True})
class CriticNetwork(base_network.Network):
""" the critic represents a mapping from state & actors action to a quality score."""
def __init__(self, namespace, actor):
super(CriticNetwork, self).__init__(namespace)
# input state to the critic is the _same_ state given to the actor.
# input action to the critic is simply the output action of the actor.
# even though when training we explicitly provide a new value for the
# input action (via the input_action placeholder) we need to be stop the gradient
# flowing to the actor since there is a path through the actor to the input_state
# too, hence we need to be explicit about cutting it (otherwise training the
# critic will attempt to train the actor too.
self.input_state = actor.input_state
self.input_action = tf.stop_gradient(actor.output_action)
with tf.variable_scope(namespace):
if opts.use_raw_pixels:
conv_net = self.simple_conv_net_on(self.input_state, opts)
# TODO: use base_network helper
hidden1 = slim.fully_connected(conv_net, 200, scope='hidden1')
hidden2 = slim.fully_connected(hidden1, 50, scope='hidden2')
concat_inputs = tf.concat(1, [hidden2, self.input_action])
final_hidden = slim.fully_connected(concat_inputs, 50, scope="hidden3")
# stack of hidden layers on flattened input; (batch,2,2,7) -> (batch,28)
flat_input_state = slim.flatten(self.input_state, scope='flat')
concat_inputs = tf.concat(1, [flat_input_state, self.input_action])
final_hidden = self.hidden_layers_starting_at(concat_inputs,
# output from critic is a single q-value
self.q_value = slim.fully_connected(scope='q_value',
def init_ops_for_training(self, target_critic):
# update critic using bellman equation; Q(s1, a) = reward + discount * Q(s2, A(s2))
# left hand side of bellman is just q_value, but let's be explicit about it...
bellman_lhs = self.q_value
# right hand side is ...
# = reward + discounted q value from target actor & critic in the non terminal case
# = reward # in the terminal case
self.reward = tf.placeholder(shape=[None, 1], dtype=tf.float32, name="critic_reward")
self.terminal_mask = tf.placeholder(shape=[None, 1], dtype=tf.float32,
self.input_state_2 = target_critic.input_state
bellman_rhs = self.reward + (self.terminal_mask * * target_critic.q_value)
# note: since we are NOT training target networks we stop gradients flowing to them
bellman_rhs = tf.stop_gradient(bellman_rhs)
# the value we are trying to mimimise is the difference between these two; the
# temporal difference we use a squared loss for optimisation and, as for actor, we
# wrap optimiser in a namespace so it's not picked up by target network variable
# handling.
self.temporal_difference = bellman_lhs - bellman_rhs
self.temporal_difference_loss = tf.reduce_mean(tf.pow(self.temporal_difference, 2))
# self.temporal_difference_loss = tf.Print(self.temporal_difference_loss, [self.temporal_difference_loss], 'temporal_difference_loss')
with tf.variable_scope("optimiser"):
# calc gradients
optimiser = tf.train.GradientDescentOptimizer(opts.critic_learning_rate)
gradients = optimiser.compute_gradients(self.temporal_difference_loss)
# potentially clip and wrap with debugging tf.Print
gradients = util.clip_and_debug_gradients(gradients, opts)
# apply
self.train_op = optimiser.apply_gradients(gradients)
def q_gradients_wrt_actions(self):
""" gradients for the q.value w.r.t just input_action; used for actor training"""
return tf.gradients(self.q_value, self.input_action)[0]
# def debug_q_value_for(self, input_state, action=None):
# feed_dict = {self.input_state: input_state}
# if action is not None:
# feed_dict[self.input_action] = action
# return np.squeeze(tf.get_default_session().run(self.q_value, feed_dict=feed_dict))
def train(self, batch):
feed_dict={self.input_state: batch.state_1,
self.input_action: batch.action,
self.reward: batch.reward,
self.terminal_mask: batch.terminal_mask,
self.input_state_2: batch.state_2,
base_network.IS_TRAINING: True})
def check_loss(self, batch):
return tf.get_default_session().run([self.temporal_difference_loss,
feed_dict={self.input_state: batch.state_1,
self.input_action: batch.action,
self.reward: batch.reward,
self.terminal_mask: batch.terminal_mask,
self.input_state_2: batch.state_2,
base_network.IS_TRAINING: False})
class DeepDeterministicPolicyGradientAgent(object):
def __init__(self, env):
self.env = env
state_shape = self.env.observation_space.shape
action_dim = self.env.action_space.shape[1]
# for now, with single machine synchronous training, use a replay memory for training.
# this replay memory stores states in a Variable (ie potentially in gpu memory)
# TODO: switch back to async training with multiple replicas (as in drivebot project)
self.replay_memory = replay_memory.ReplayMemory(opts.replay_memory_size,
state_shape, action_dim)
# s1 and s2 placeholders
batched_state_shape = [None] + list(state_shape)
s1 = tf.placeholder(shape=batched_state_shape, dtype=tf.float32)
s2 = tf.placeholder(shape=batched_state_shape, dtype=tf.float32)
# initialise base models for actor / critic and their corresponding target networks
# target_actor is never used for online sampling so doesn't need explore noise. = ActorNetwork("actor", s1, action_dim)
self.critic = CriticNetwork("critic",
self.target_actor = ActorNetwork("target_actor", s2, action_dim)
self.target_critic = CriticNetwork("target_critic", self.target_actor)
# setup training ops;
# training actor requires the critic (for getting gradients)
# training critic requires target_critic (for RHS of bellman update)
def post_var_init_setup(self):
# prepopulate replay memory (if configured to do so)
if opts.event_log_in:
# hook networks up to their targets
# ( does one off clobber to init all vars in target network )
self.target_actor.set_as_target_network_for(, opts.target_update_rate)
self.target_critic.set_as_target_network_for(self.critic, opts.target_update_rate)
def run_training(self, max_num_actions, max_run_time, batch_size, batches_per_step,
# log start time, in case we are limiting by time...
start_time = time.time()
# run for some max number of actions
num_actions_taken = 0
n = 0
while True:
rewards = []
losses = []
# run an episode
if opts.dont_do_rollouts:
# _not_ gathering experience online
# start a new episode
state_1 = self.env.reset()
# prepare data for updating replay memory at end of episode
initial_state = np.copy(state_1)
action_reward_state_sequence = []
done = False
while not done:
# choose action
action =, add_noise=True)
# take action step in env
state_2, reward, done, _ = self.env.step(action)
# cache for adding to replay memory
action_reward_state_sequence.append((action, reward, np.copy(state_2)))
# roll state for next step.
state_1 = state_2
# at end of episode update replay memory
self.replay_memory.add_episode(initial_state, action_reward_state_sequence)
# do a training step (after waiting for buffer to fill a bit...)
if self.replay_memory.size() > opts.replay_memory_burn_in:
# run a set of batches
for _ in xrange(batches_per_step):
batch = self.replay_memory.batch(batch_size)
# update target nets
# do debug (if requested) on last batch
print "-----"
#print "state_1", state_1
print "action\n", batch.action.T
print "reward ", batch.reward.T
print "terminal_mask ", batch.terminal_mask.T
#print "state_2", state_2
td_loss, td, q_value = self.critic.check_loss(batch)
print "temporal_difference_loss", td_loss
print "temporal_difference", td.T
print "q_value", q_value.T
# dump some stats and progress info
stats = collections.OrderedDict()
stats["time"] = time.time()
stats["n"] = n
stats["mean_losses"] = float(np.mean(losses))
stats["total_reward"] = np.sum(rewards)
stats["episode_len"] = len(rewards)
stats["replay_memory_stats"] = self.replay_memory.current_stats()
print "STATS %s\t%s" % ('%Y-%m-%d %H:%M:%S'),
n += 1
# save if required
if saver_util is not None:
# emit occasional eval
if VERBOSE_DEBUG or n % 10 == 0:
# dump weights once if requested
# exit when finished
num_actions_taken += len(rewards)
if max_num_actions > 0 and num_actions_taken > max_num_actions:
if max_run_time > 0 and time.time() > start_time + max_run_time:
def run_eval(self, num_episodes, add_noise=False):
""" run num_episodes of eval and output episode length and rewards """
for i in xrange(num_episodes):
state = self.env.reset()
total_reward = 0
steps = 0
done = False
while not done:
action =, add_noise)
state, reward, done, _ = self.env.step(action)
print "EVALSTEP r%s %s %s %s %s" % (i, steps, np.squeeze(action), np.linalg.norm(action), reward)
total_reward += reward
steps += 1
print "EVAL", i, steps, total_reward
def debug_dump_network_weights(self):
fn = "/tmp/weights.%s" % time.time()
with open(fn, "w") as f:
f.write("DUMP time %s\n" % time.time())
for var in tf.all_variables():
f.write("VAR %s %s\n" % (, var.get_shape()))
f.write("%s\n" % var.eval())
print "weights written to", fn
def main():
config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
# config.log_device_placement = True
with tf.Session(config=config) as sess:
agent = DeepDeterministicPolicyGradientAgent(env=env)
# setup saver util and either load latest ckpt or init variables
saver_util = None
if opts.ckpt_dir is not None:
saver_util = util.SaverUtil(sess, opts.ckpt_dir, opts.ckpt_freq)
for v in tf.all_variables():
print >>sys.stderr,, util.shape_and_product_of(v)
# now that we've either init'd from scratch, or loaded up a checkpoint,
# we can do any required post init work.
# run either eval or training
if opts.num_eval > 0:
agent.run_eval(opts.num_eval, opts.eval_action_noise)
agent.run_training(opts.max_num_actions, opts.max_run_time,
opts.batch_size, opts.batches_per_step,
if saver_util is not None:
env.reset() # just to flush logging, clumsy :/
if __name__ == "__main__":