def evaluation(session, graph_ops, saver):
saver.restore(session, FLAGS.checkpoint_path)
print "Restored model weights from ", FLAGS.checkpoint_path
monitor_env = gym.make(FLAGS.game)
monitor_env.monitor.start(FLAGS.eval_dir + "/" + FLAGS.experiment +
"/eval")
# Unpack graph ops
s = graph_ops["s"]
q_values = graph_ops["q_values"]
# Wrap env with AtariEnvironment helper class
env = AtariEnvironment(gym_env=monitor_env,
resized_width=FLAGS.resized_width,
resized_height=FLAGS.resized_height,
agent_history_length=FLAGS.agent_history_length)
for i_episode in xrange(FLAGS.num_eval_episodes):
s_t = env.get_initial_state()
ep_reward = 0
terminal = False
while not terminal:
monitor_env.render()
readout_t = q_values.eval(session=session, feed_dict={s: [s_t]})
action_index = np.argmax(readout_t)
s_t1, r_t, terminal, info = env.step(action_index)
s_t = s_t1
ep_reward += r_t
print ep_reward
monitor_env.monitor.close()
def evaluation(session, graph_ops, saver):
saver.restore(session, CHECKPOINT_NAME)
print "Restored model weights from ", CHECKPOINT_NAME
monitor_env = gym.make(GAME)
monitor_env.monitor.start('/tmp/'+EXPERIMENT_NAME+"/eval")
# Unpack graph ops
s, a_t, R_t, learning_rate, minimize, p_network, v_network = graph_ops
# Wrap env with AtariEnvironment helper class
env = AtariEnvironment(gym_env=monitor_env, resized_width=RESIZED_WIDTH, resized_height=RESIZED_HEIGHT, agent_history_length=AGENT_HISTORY_LENGTH)
for i_episode in xrange(100):
s_t = env.get_initial_state()
ep_reward = 0
terminal = False
while not terminal:
monitor_env.render()
# Forward the deep q network, get Q(s,a) values
probs = p_network.eval(session = session, feed_dict = {s : [s_t]})[0]
action_index = sample_policy_action(ACTIONS, probs)
s_t1, r_t, terminal, info = env.step(action_index)
s_t = s_t1
ep_reward += r_t
print ep_reward
monitor_env.monitor.close()
def evaluation(session, graph_ops, saver):
saver.restore(session, CHECKPOINT_NAME)
print "Restored model weights from ", CHECKPOINT_NAME
monitor_env = gym.make(GAME)
monitor_env.monitor.start('/tmp/' + EXPERIMENT_NAME + "/eval")
# Unpack graph ops
s, a_t, R_t, minimize, p_network, v_network = graph_ops
# Wrap env with AtariEnvironment helper class
env = AtariEnvironment(gym_env=monitor_env,
resized_width=RESIZED_WIDTH,
resized_height=RESIZED_HEIGHT,
agent_history_length=AGENT_HISTORY_LENGTH)
for i_episode in xrange(100):
s_t = env.get_initial_state()
ep_reward = 0
terminal = False
while not terminal:
monitor_env.render()
# Forward the deep q network, get Q(s,a) values
probs = p_network.eval(session=session, feed_dict={s: [s_t]})[0]
action_index = sample_policy_action(ACTIONS, probs)
s_t1, r_t, terminal, info = env.step(action_index)
s_t = s_t1
ep_reward += r_t
print ep_reward
monitor_env.monitor.close()
def evaluation(session, graph_ops, saver):
saver.restore(session, FLAGS.checkpoint_path)
print "Restored model weights from ", FLAGS.checkpoint_path
monitor_env = gym.make(FLAGS.game)
monitor_env.monitor.start(FLAGS.eval_dir+"/"+FLAGS.experiment+"/eval")
# Unpack graph ops
s = graph_ops["s"]
q_values = graph_ops["q_values"]
# Wrap env with AtariEnvironment helper class
env = AtariEnvironment(gym_env=monitor_env, resized_width=FLAGS.resized_width, resized_height=FLAGS.resized_height, agent_history_length=FLAGS.agent_history_length)
for i_episode in xrange(FLAGS.num_eval_episodes):
s_t = env.get_initial_state()
ep_reward = 0
terminal = False
while not terminal:
monitor_env.render()
readout_t = q_values.eval(session = session, feed_dict = {s : [s_t]})
action_index = np.argmax(readout_t)
s_t1, r_t, terminal, info = env.step(action_index)
s_t = s_t1
ep_reward += r_t
print ep_reward
monitor_env.monitor.close()
def __init__(self, id, prediction_q, training_q, episode_log_q):
super(Agent, self).__init__(name="Agent_{}".format(id))
self.id = id
self.prediction_q = prediction_q
self.training_q = training_q
self.episode_log_q = episode_log_q
gym_env = gym.make(FLAGS.game)
gym_env.seed(FLAGS.seed)
self.env = AtariEnvironment(
gym_env=gym_env,
resized_width=FLAGS.resized_width,
resized_height=FLAGS.resized_height,
agent_history_length=FLAGS.agent_history_length)
self.nb_actions = len(self.env.gym_actions)
self.wait_q = Queue(maxsize=1)
self.stop = Value('i', 0)
def evaluation(session, graph_ops, saver):
saver.restore(session, FLAGS.checkpoint_path)
print("Restored model weights from ", FLAGS.checkpoint_path)
monitor_env = gym.make(FLAGS.game)
gym.wrappers.Monitor(monitor_env,
FLAGS.eval_dir + "/" + FLAGS.experiment + "/eval")
# Unpack graph ops
s = graph_ops["s"]
q_values = graph_ops["q_values"]
# Wrap env with AtariEnvironment helper class
if env_type in {'atari'}:
env = AtariEnvironment(gym_env=monitor_env,
resized_width=FLAGS.resized_width,
resized_height=FLAGS.resized_height,
agent_history_length=FLAGS.agent_history_length)
else:
env = CustomEnvironment(
gym_env=monitor_env,
input_size=FLAGS.input_size,
agent_history_length=FLAGS.agent_history_length,
extra_args={
'init_with_args': FLAGS.init_with_args,
'setting_file_path': FLAGS.setting_file_path
})
for i_episode in range(FLAGS.num_eval_episodes):
s_t = env.get_initial_state()
ep_reward = 0
terminal = False
while not terminal:
monitor_env.render()
readout_t = q_values.eval(session=session, feed_dict={s: [s_t]})
action_index = np.argmax(readout_t)
print("action", action_index)
s_t1, r_t, terminal, info = env.step(action_index)
s_t = s_t1
ep_reward += r_t
print(ep_reward)
monitor_env.monitor.close()
def __init__(self, id, prediction_q, training_q, episode_log_q):
super(Agent, self).__init__(name="Agent_{}".format(id))
self.id = id
self.prediction_q = prediction_q
self.training_q = training_q
self.episode_log_q = episode_log_q
gym_env = gym.make(FLAGS.game)
gym_env.seed(FLAGS.seed)
self.env = AtariEnvironment(gym_env=gym_env, resized_width=FLAGS.resized_width,
resized_height=FLAGS.resized_height,
agent_history_length=FLAGS.agent_history_length)
self.nb_actions = len(self.env.gym_actions)
self.wait_q = Queue(maxsize=1)
self.stop = Value('i', 0)
def main():
env = gym.make(ENV_NAME)
env = AtariEnvironment(gym_env=env,
resized_width=RESIZED_WIDTH,
resized_height=RESIZED_HEIGHT,
sequence_length=SEQUENCE_LENGTH)
g = tf.Graph()
with g.as_default(), tf.Session() as sess:
K.set_session(sess)
graph_ops = build_graph()
sess.run(tf.global_variables_initializer())
times, rewards = train(env, sess, graph_ops)
print(times)
print(rewards)
visualize(np.arange(len(times)), times, "times.png")
visualize(np.arange(len(rewards)), rewards, "rewards.png")
def run():
tf.reset_default_graph()
with tf.Session() as sess:
with tf.device("/cpu:0"):
global_step = tf.Variable(0, dtype=tf.int32, name='global_episodes', trainable=False)
# optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.lr)
optimizer = tf.train.RMSPropOptimizer(FLAGS.lr, 0.99, 0.0, 1e-6)
gym_env_monitor = gym.make(FLAGS.game)
gym_env_monitor.seed(FLAGS.seed)
gym_env_monitor_wrapper = AtariEnvironment(gym_env=gym_env_monitor, resized_width=FLAGS.resized_width,
resized_height=FLAGS.resized_height,
agent_history_length=FLAGS.agent_history_length)
nb_actions = len(gym_env_monitor_wrapper.gym_actions)
if FLAGS.lstm:
global_network = ACNetworkLSTM('global', nb_actions, None)
else:
global_network = ACNetwork('global', nb_actions, None)
saver = tf.train.Saver(max_to_keep=5)
if FLAGS.resume:
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
print("Loading Model from {}".format(ckpt.model_checkpoint_path))
saver.restore(sess, ckpt.model_checkpoint_path)
else:
sess.run(tf.global_variables_initializer())
pe = PolicyMonitor(
game=gym_env_monitor_wrapper,
nb_actions=nb_actions,
optimizer=optimizer,
global_step=global_step
)
pe.eval_1000(sess)
class Agent(Process):
def __init__(self, id, prediction_q, training_q, episode_log_q):
super(Agent, self).__init__(name="Agent_{}".format(id))
self.id = id
self.prediction_q = prediction_q
self.training_q = training_q
self.episode_log_q = episode_log_q
gym_env = gym.make(FLAGS.game)
gym_env.seed(FLAGS.seed)
self.env = AtariEnvironment(
gym_env=gym_env,
resized_width=FLAGS.resized_width,
resized_height=FLAGS.resized_height,
agent_history_length=FLAGS.agent_history_length)
self.nb_actions = len(self.env.gym_actions)
self.wait_q = Queue(maxsize=1)
self.stop = Value('i', 0)
def run(self):
time.sleep(np.random.rand())
while not self.stop.value:
if FLAGS.verbose:
print("Agent_{} started a new episode".format(self.id))
# total_reward = 0
# total_length = 0
for episode_buffer, episode_reward, episode_length in self.run_episode_generator(
):
if FLAGS.verbose:
print("Agent_{} puts a new episode in the training queue".
format(self.id))
self.training_q.put(episode_buffer)
print(
"Agent_{} fished an episode and logs the result in the logs queue"
.format(self.id))
self.episode_log_q.put(
[datetime.now(), episode_reward, episode_length])
def run_episode_generator(self):
s, _ = self.env.get_initial_state()
d = False
episode_buffer = []
episode_reward = 0
episode_step_count = 0
while not d:
self.prediction_q.put((self.id, s))
pi, v = self.wait_q.get()
a = np.random.choice(pi[0], p=pi[0])
a = np.argmax(pi == a)
s1, r, d, info = self.env.step(a)
r = np.clip(r, -1, 1)
episode_buffer.append([s, a, pi, r, s1, d, v[0, 0]])
episode_reward += r
episode_step_count += 1
s = s1
if len(episode_buffer) == FLAGS.max_episode_buffer_size and not d:
self.prediction_q.put((self.id, s))
pi, v1 = self.wait_q.get()
updated_episode_buffer = self.get_training_data(
episode_buffer, v1)
yield updated_episode_buffer, episode_reward, episode_step_count
if d:
break
if len(episode_buffer) != 0:
updated_episode_buffer = self.get_training_data(episode_buffer, 0)
yield updated_episode_buffer, episode_reward, episode_step_count
def discount(self, x):
return lfilter([1], [1, -FLAGS.gamma], x[::-1], axis=0)[::-1]
def get_training_data(self, rollout, bootstrap_value):
rollout = np.array(rollout)
observations = rollout[:, 0]
actions = rollout[:, 1]
pis = rollout[:, 2]
rewards = rollout[:, 3]
next_observations = rollout[:, 4]
values = rollout[:, 5]
rewards_plus = np.asarray(rewards.tolist() + [bootstrap_value])
discounted_rewards = self.discount(rewards_plus, FLAGS.gamma)[:-1]
value_plus = np.asarray(values.tolist() + [bootstrap_value])
policy_target = discounted_rewards - value_plus[:-1]
rollout.extend([discounted_rewards])
def actor_learner_thread(thread_id, env, session, graph_ops, num_actions,
summary_ops, saver):
"""
Actor-learner thread implementing asynchronous one-step Q-learning, as specified
in algorithm 1 here: http://arxiv.org/pdf/1602.01783v1.pdf.
"""
global TMAX, T
# Unpack graph ops
s = graph_ops["s"]
q_values = graph_ops["q_values"]
st = graph_ops["st"]
target_q_values = graph_ops["target_q_values"]
reset_target_network_params = graph_ops["reset_target_network_params"]
a = graph_ops["a"]
y = graph_ops["y"]
grad_update = graph_ops["grad_update"]
summary_placeholders, update_ops, summary_op = summary_ops
# Wrap env with AtariEnvironment helper class
env = AtariEnvironment(gym_env=env,
resized_width=FLAGS.resized_width,
resized_height=FLAGS.resized_height,
agent_history_length=FLAGS.agent_history_length)
# Initialize network gradients
s_batch = []
a_batch = []
y_batch = []
final_epsilon = sample_final_epsilon()
initial_epsilon = 1.0
epsilon = 1.0
print "Starting thread ", thread_id, "with final epsilon ", final_epsilon
time.sleep(3 * thread_id)
t = 0
while T < TMAX:
# Get initial game observation
s_t = env.get_initial_state()
terminal = False
# Set up per-episode counters
ep_reward = 0
episode_ave_max_q = 0
ep_t = 0
while True:
# Forward the deep q network, get Q(s,a) values
readout_t = q_values.eval(session=session, feed_dict={s: [s_t]})
# Choose next action based on e-greedy policy
a_t = np.zeros([num_actions])
action_index = 0
if random.random() <= epsilon:
action_index = random.randrange(num_actions)
else:
action_index = np.argmax(readout_t)
a_t[action_index] = 1
# Scale down epsilon
if epsilon > final_epsilon:
epsilon -= (initial_epsilon -
final_epsilon) / FLAGS.anneal_epsilon_timesteps
# Gym excecutes action in game environment on behalf of actor-learner
s_t1, r_t, terminal, info = env.step(action_index)
# Accumulate gradients
readout_j1 = target_q_values.eval(session=session,
feed_dict={st: [s_t1]})
clipped_r_t = np.clip(r_t, -1, 1)
if terminal:
y_batch.append(clipped_r_t)
else:
y_batch.append(clipped_r_t + FLAGS.gamma * np.max(readout_j1))
a_batch.append(a_t)
s_batch.append(s_t)
# Update the state and counters
s_t = s_t1
T += 1
t += 1
ep_t += 1
ep_reward += r_t
episode_ave_max_q += np.max(readout_t)
# Optionally update target network
if T % FLAGS.target_network_update_frequency == 0:
session.run(reset_target_network_params)
# Optionally update online network
if t % FLAGS.network_update_frequency == 0 or terminal:
if s_batch:
session.run(grad_update,
feed_dict={
y: y_batch,
a: a_batch,
s: s_batch
})
# Clear gradients
s_batch = []
a_batch = []
y_batch = []
# Save model progress
if t % FLAGS.checkpoint_interval == 0:
saver.save(session,
FLAGS.checkpoint_dir + "/" + FLAGS.experiment +
".ckpt",
global_step=t)
# Print end of episode stats
if terminal:
stats = [ep_reward, episode_ave_max_q / float(ep_t), epsilon]
for i in range(len(stats)):
session.run(
update_ops[i],
feed_dict={summary_placeholders[i]: float(stats[i])})
print "THREAD:", thread_id, "/ TIME", T, "/ TIMESTEP", t, "/ EPSILON", epsilon, "/ REWARD", ep_reward, "/ Q_MAX %.4f" % (
episode_ave_max_q /
float(ep_t)), "/ EPSILON PROGRESS", t / float(
FLAGS.anneal_epsilon_timesteps)
break
parser.add_argument("--save-model-freq", type=int, default=10000, help="save the model once per 10000 training sessions")
parser.add_argument("--observation-steps", type=int, default=50000, help="train only after this many stesp (=4 frames)")
parser.add_argument("--learning-rate", type=float, default=0.00025, help="learning rate (step size for optimization algo)")
parser.add_argument("--target-model-update-freq", type=int, default=10000, help="how often (in steps) to update the target model. Note nature paper says this is in 'number of parameter updates' but their code says steps. see tinyurl.com/hokp4y8")
parser.add_argument("--model", help="tensorflow model checkpoint file to initialize from")
parser.add_argument("rom", help="rom file to run")
args = parser.parse_args()
print 'Arguments: %s' % (args)
baseOutputDir = 'game-out-' + time.strftime("%Y-%m-%d-%H-%M-%S")
os.makedirs(baseOutputDir)
State.setup(args)
environment = AtariEnvironment(args, baseOutputDir)
dqn = dqn.DeepQNetwork(environment.getNumActions(), baseOutputDir, args)
replayMemory = replay.ReplayMemory(args)
def runEpoch(minEpochSteps, evalWithEpsilon=None):
stepStart = environment.getStepNumber()
isTraining = True if evalWithEpsilon is None else False
startGameNumber = environment.getGameNumber()
epochTotalScore = 0
while environment.getStepNumber() - stepStart < minEpochSteps:
startTime = lastLogTime = time.time()
stateReward = 0
help=
"how often (in steps) to update the target model. Note nature paper says this is in 'number of parameter updates' but their code says steps. see tinyurl.com/hokp4y8"
)
parser.add_argument("--model",
help="tensorflow model checkpoint file to initialize from")
parser.add_argument("rom", help="rom file to run")
args = parser.parse_args()
print 'Arguments: %s' % (args)
baseOutputDir = 'game-out-' + time.strftime("%Y-%m-%d-%H-%M-%S")
os.makedirs(baseOutputDir)
State.setup(args)
environment = AtariEnvironment(args, baseOutputDir)
dqn = dqn.DeepQNetwork(environment.getNumActions(), baseOutputDir, args)
replayMemory = replay.ReplayMemory(args)
def runEpoch(minEpochSteps, evalWithEpsilon=None):
stepStart = environment.getStepNumber()
isTraining = True if evalWithEpsilon is None else False
startGameNumber = environment.getGameNumber()
epochTotalScore = 0
while environment.getStepNumber() - stepStart < minEpochSteps:
startTime = lastLogTime = time.time()
def actor_learner_thread(num, env, session, graph_ops, summary_ops, saver):
# We use global shared counter T, and TMAX constant
global TMAX, T
# Unpack graph ops
s, a, R, minimize, p_network, v_network = graph_ops
# Unpack tensorboard summary stuff
r_summary_placeholder, update_ep_reward, val_summary_placeholder, update_ep_val, summary_op = summary_ops
# Wrap env with AtariEnvironment helper class
env = AtariEnvironment(gym_env=env, resized_width=RESIZED_WIDTH, resized_height=RESIZED_HEIGHT, agent_history_length=AGENT_HISTORY_LENGTH)
time.sleep(5*num)
# Set up per-episode counters
ep_reward = 0
ep_avg_v = 0
v_steps = 0
ep_t = 0
probs_summary_t = 0
s_t = env.get_initial_state()
terminal = False
while T < TMAX:
s_batch = []
past_rewards = []
a_batch = []
t = 0
t_start = t
while not (terminal or ((t - t_start) == t_max)):
# Perform action a_t according to policy pi(a_t | s_t)
probs = session.run(p_network, feed_dict={s: [s_t]})[0]
action_index = sample_policy_action(ACTIONS, probs)
a_t = np.zeros([ACTIONS])
a_t[action_index] = 1
if probs_summary_t % 100 == 0:
print "P, ", np.max(probs), "V ", session.run(v_network, feed_dict={s: [s_t]})[0][0]
s_batch.append(s_t)
a_batch.append(a_t)
s_t1, r_t, terminal, info = env.step(action_index)
ep_reward += r_t
r_t = np.clip(r_t, -1, 1)
past_rewards.append(r_t)
t += 1
T += 1
ep_t += 1
probs_summary_t += 1
s_t = s_t1
if terminal:
R_t = 0
else:
R_t = session.run(v_network, feed_dict={s: [s_t]})[0][0] # Bootstrap from last state
R_batch = np.zeros(t)
for i in reversed(range(t_start, t)):
R_t = past_rewards[i] + GAMMA * R_t
R_batch[i] = R_t
session.run(minimize, feed_dict={R : R_batch,
a : a_batch,
s : s_batch})
# Save progress every 5000 iterations
if T % CHECKPOINT_INTERVAL == 0:
saver.save(session, CHECKPOINT_SAVE_PATH, global_step = T)
if terminal:
# Episode ended, collect stats and reset game
session.run(update_ep_reward, feed_dict={r_summary_placeholder: ep_reward})
print "THREAD:", num, "/ TIME", T, "/ REWARD", ep_reward
s_t = env.get_initial_state()
terminal = False
# Reset per-episode counters
ep_reward = 0
ep_t = 0
def run():
recreate_directory_structure()
tf.reset_default_graph()
sess = tf.Session()
# sess = tf_debug.LocalCLIDebugWrapperSession(sess)
# sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
with sess:
with tf.device("/cpu:0"):
global_step = tf.Variable(0,
dtype=tf.int32,
name='global_episodes',
trainable=False)
# optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.lr)
optimizer = tf.train.RMSPropOptimizer(FLAGS.lr, 0.99, 0.0, 1e-6)
num_workers = FLAGS.nb_concurrent
#num_workers = multiprocessing.cpu_count() - 1
workers = []
envs = []
for i in range(num_workers):
gym_env = gym.make(FLAGS.game)
if FLAGS.seed:
gym_env.seed(FLAGS.seed)
if FLAGS.monitor:
gym_env = gym.wrappers.Monitor(
gym_env,
FLAGS.experiments_dir + '/worker_{}'.format(i))
this_env = AtariEnvironment(
gym_env=gym_env,
resized_width=FLAGS.resized_width,
resized_height=FLAGS.resized_height,
agent_history_length=FLAGS.agent_history_length)
envs.append(this_env)
nb_actions = len(envs[0].gym_actions)
if FLAGS.lstm:
global_network = ACNetworkLSTM('global', nb_actions, None)
else:
global_network = ACNetwork('global', nb_actions, None)
for i in range(num_workers):
workers.append(
Worker(envs[i], sess, i, nb_actions, optimizer,
global_step))
saver = tf.train.Saver(max_to_keep=5)
# gym_env_monitor = gym.make(FLAGS.game)
# gym_env_monitor.seed(FLAGS.seed)
# gym_env_monitor_wrapper = AtariEnvironment(gym_env=gym_env_monitor, resized_width=FLAGS.resized_width,
# resized_height=FLAGS.resized_height,
# agent_history_length=FLAGS.agent_history_length)
# nb_actions = len(gym_env_monitor_wrapper.gym_actions)
# pe = PolicyMonitor(
# game=gym_env_monitor_wrapper,
# nb_actions=nb_actions,
# optimizer=optimizer,
# global_step=global_step
# )
coord = tf.train.Coordinator()
if FLAGS.resume:
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
print("Loading Model from {}".format(ckpt.model_checkpoint_path))
saver.restore(sess, ckpt.model_checkpoint_path)
else:
sess.run(tf.global_variables_initializer())
worker_threads = []
for worker in workers:
t = threading.Thread(target=(lambda: worker.play(coord, saver)))
t.start()
worker_threads.append(t)
# Start a thread for policy eval task
# monitor_thread = threading.Thread(target=lambda: pe.continuous_eval(FLAGS.eval_every, sess, coord))
# monitor_thread.start()
import time
while True:
if FLAGS.show_training:
for env in envs:
# time.sleep(1)
# with main_lock:
env.env.render()
coord.join(worker_threads)
class Agent(Process):
def __init__(self, id, prediction_q, training_q, episode_log_q):
super(Agent, self).__init__(name="Agent_{}".format(id))
self.id = id
self.prediction_q = prediction_q
self.training_q = training_q
self.episode_log_q = episode_log_q
gym_env = gym.make(FLAGS.game)
gym_env.seed(FLAGS.seed)
self.env = AtariEnvironment(gym_env=gym_env, resized_width=FLAGS.resized_width,
resized_height=FLAGS.resized_height,
agent_history_length=FLAGS.agent_history_length)
self.nb_actions = len(self.env.gym_actions)
self.wait_q = Queue(maxsize=1)
self.stop = Value('i', 0)
def run(self):
time.sleep(np.random.rand())
while not self.stop.value:
if FLAGS.verbose:
print("Agent_{} started a new episode".format(self.id))
# total_reward = 0
# total_length = 0
for episode_buffer, episode_reward, episode_length in self.run_episode_generator():
if FLAGS.verbose:
print("Agent_{} puts a new episode in the training queue".format(self.id))
self.training_q.put(episode_buffer)
print("Agent_{} fished an episode and logs the result in the logs queue".format(self.id))
self.episode_log_q.put([datetime.now(), episode_reward, episode_length])
def run_episode_generator(self):
s, _ = self.env.get_initial_state()
d = False
episode_buffer = []
episode_reward = 0
episode_step_count = 0
while not d:
self.prediction_q.put((self.id, s))
pi, v = self.wait_q.get()
a = np.random.choice(pi[0], p=pi[0])
a = np.argmax(pi == a)
s1, r, d, info = self.env.step(a)
r = np.clip(r, -1, 1)
episode_buffer.append([s, a, pi, r, s1, d, v[0, 0]])
episode_reward += r
episode_step_count += 1
s = s1
if len(episode_buffer) == FLAGS.max_episode_buffer_size and not d:
self.prediction_q.put((self.id, s))
pi, v1 = self.wait_q.get()
updated_episode_buffer = self.get_training_data(episode_buffer, v1)
yield updated_episode_buffer, episode_reward, episode_step_count
if d:
break
if len(episode_buffer) != 0:
updated_episode_buffer = self.get_training_data(episode_buffer, 0)
yield updated_episode_buffer, episode_reward, episode_step_count
def discount(self, x):
return lfilter([1], [1, -FLAGS.gamma], x[::-1], axis=0)[::-1]
def get_training_data(self, rollout, bootstrap_value):
rollout = np.array(rollout)
observations = rollout[:, 0]
actions = rollout[:, 1]
pis = rollout[:, 2]
rewards = rollout[:, 3]
next_observations = rollout[:, 4]
values = rollout[:, 5]
rewards_plus = np.asarray(rewards.tolist() + [bootstrap_value])
discounted_rewards = self.discount(rewards_plus, FLAGS.gamma)[:-1]
value_plus = np.asarray(values.tolist() + [bootstrap_value])
policy_target = discounted_rewards - value_plus[:-1]
rollout.extend([discounted_rewards])
def actor_learner_thread(thread_id, env, session, graph_ops, num_actions, summary_ops, saver):
"""
Actor-learner thread implementing asynchronous one-step Q-learning, as specified
in algorithm 1 here: http://arxiv.org/pdf/1602.01783v1.pdf.
"""
global TMAX, T
# Unpack graph ops
s = graph_ops["s"]
q_values = graph_ops["q_values"]
st = graph_ops["st"]
target_q_values = graph_ops["target_q_values"]
reset_target_network_params = graph_ops["reset_target_network_params"]
a = graph_ops["a"]
y = graph_ops["y"]
grad_update = graph_ops["grad_update"]
summary_placeholders, update_ops, summary_op = summary_ops
# Wrap env with AtariEnvironment helper class
env = AtariEnvironment(gym_env=env, resized_width=FLAGS.resized_width, resized_height=FLAGS.resized_height, agent_history_length=FLAGS.agent_history_length)
# Initialize network gradients
s_batch = []
a_batch = []
y_batch = []
final_epsilon = sample_final_epsilon()
initial_epsilon = 1.0
epsilon = 1.0
print "Starting thread ", thread_id, "with final epsilon ", final_epsilon
time.sleep(3*thread_id)
t = 0
while T < TMAX:
# Get initial game observation
s_t = env.get_initial_state()
terminal = False
# Set up per-episode counters
ep_reward = 0
episode_ave_max_q = 0
ep_t = 0
while True:
# Forward the deep q network, get Q(s,a) values
readout_t = q_values.eval(session = session, feed_dict = {s : [s_t]})
# Choose next action based on e-greedy policy
a_t = np.zeros([num_actions])
action_index = 0
if random.random() <= epsilon:
action_index = random.randrange(num_actions)
else:
action_index = np.argmax(readout_t)
a_t[action_index] = 1
# Scale down epsilon
if epsilon > final_epsilon:
epsilon -= (initial_epsilon - final_epsilon) / FLAGS.anneal_epsilon_timesteps
# Gym excecutes action in game environment on behalf of actor-learner
s_t1, r_t, terminal, info = env.step(action_index)
# Accumulate gradients
readout_j1 = target_q_values.eval(session = session, feed_dict = {st : [s_t1]})
clipped_r_t = np.clip(r_t, -1, 1)
if terminal:
y_batch.append(clipped_r_t)
else:
y_batch.append(clipped_r_t + FLAGS.gamma * np.max(readout_j1))
a_batch.append(a_t)
s_batch.append(s_t)
# Update the state and counters
s_t = s_t1
T += 1
t += 1
ep_t += 1
ep_reward += r_t
episode_ave_max_q += np.max(readout_t)
# Optionally update target network
if T % FLAGS.target_network_update_frequency == 0:
session.run(reset_target_network_params)
# Optionally update online network
if t % FLAGS.network_update_frequency == 0 or terminal:
if s_batch:
session.run(grad_update, feed_dict = {y : y_batch,
a : a_batch,
s : s_batch})
# Clear gradients
s_batch = []
a_batch = []
y_batch = []
# Save model progress
if t % FLAGS.checkpoint_interval == 0:
saver.save(session, FLAGS.checkpoint_dir+"/"+FLAGS.experiment+".ckpt", global_step = t)
# Print end of episode stats
if terminal:
stats = [ep_reward, episode_ave_max_q/float(ep_t), epsilon]
for i in range(len(stats)):
session.run(update_ops[i], feed_dict={summary_placeholders[i]:float(stats[i])})
print "THREAD:", thread_id, "/ TIME", T, "/ TIMESTEP", t, "/ EPSILON", epsilon, "/ REWARD", ep_reward, "/ Q_MAX %.4f" % (episode_ave_max_q/float(ep_t)), "/ EPSILON PROGRESS", t/float(FLAGS.anneal_epsilon_timesteps)
break
def main(_):
# Reproducability
tf.reset_default_graph()
np.random.seed(cfg.random_seed)
tf.set_random_seed(cfg.random_seed)
# Logging
summary_writer = tf.summary.FileWriter(cfg.log_dir)
if not cfg.evaluate and not tf.gfile.Exists(cfg.save_dir):
tf.gfile.MakeDirs(cfg.save_dir)
else:
assert tf.gfile.Exists(cfg.save_dir)
# TODO handel this
episode_results_path = os.path.join(cfg.log_dir, "episodeResults.csv")
episode_results = tf.gfile.GFile(episode_results_path, "w")
episode_results.write("model_freq={},save_dir={}".format(
cfg.model_freq, cfg.save_dir))
episode_results.write("episode,reward,steps\n")
episode_results.flush()
# Setup ALE and DQN graph
obs_shape = (84, 84, 1)
input_height, input_width, _ = obs_shape
dqn = DQN(input_height, input_width, cfg.num_actions)
# Global step
global_step = tf.train.get_or_create_global_step()
increment_step = tf.assign_add(global_step, 1)
# Save all variables
vars_to_save = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope="agent/q")
vars_to_save.append(global_step)
saver = tf.train.Saver(var_list=vars_to_save)
# Handle loading specific variables
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
sess = tf.Session(config=sess_config)
restore_or_initialize_weights(sess, dqn, saver)
sess.run(dqn.copy_to_target)
if cfg.evaluate:
# if in evaluation mode, saver is no longer needed
saver = None
# ##### Restoring AEs ########
if not cfg.evaluate:
vaes = create_generative_models(sess)
image_summaries = []
image_summaries_ph = tf.placeholder(tf.float32,
shape=(4, 84, 84, 4),
name="image_summaries_placeholder")
for i in range(4):
for j in range(4):
image_summaries.append(
tf.summary.image(
"VAE_OUT_{}_{}".format(i, j),
tf.reshape(image_summaries_ph[i, :, :, j],
(1, 84, 84, 1))))
# ############################
if not cfg.evaluate:
summary_writer.add_graph(tf.get_default_graph())
summary_writer.add_graph(vaes[0].graph)
summary_writer.add_graph(vaes[1].graph)
summary_writer.add_graph(vaes[2].graph)
summary_writer.flush()
# Initialize ALE
postprocess_frame = lambda frame: sess.run(dqn.process_frame,
feed_dict={dqn.image: frame})
env = AtariEnvironment(obs_shape, postprocess_frame)
# Replay buffer
if not cfg.evaluate:
replay_buffer = ExperienceReplay(cfg.replay_buffer_size, obs_shape)
# Perform random policy to get some training data
with tqdm(total=cfg.seed_frames,
disable=cfg.disable_progress or cfg.evaluate) as pbar:
seed_steps = 0
while seed_steps * cfg.frame_skip < cfg.seed_frames and not cfg.evaluate:
action = np.random.randint(cfg.num_actions)
reward, next_state, terminal = env.act(action)
seed_steps += 1
replay_buffer.append(next_state[:, :, -1, np.newaxis], action,
reward, terminal)
if terminal:
pbar.update(env.episode_frames)
env.reset(inc_episode_count=False)
if cfg.evaluate:
assert cfg.max_episode_count > 0
else:
assert len(replay_buffer) >= cfg.seed_frames // cfg.frame_skip
# Main training loop
steps = tf.train.global_step(sess, global_step)
env.reset(inc_episode_count=False)
terminal = False
total = cfg.max_episode_count if cfg.evaluate else cfg.num_frames
with tqdm(total=total, disable=cfg.disable_progress) as pbar:
# Loop while we haven't observed our max frame number
# If we are at our max frame number we will finish the current episode
while (not (
# We must be evaluating or observed the last frame
# As well as be terminal
# As well as seen the maximum episode number
(steps * cfg.frame_skip > cfg.num_frames or cfg.evaluate)
and terminal and env.episode_count >= cfg.max_episode_count)):
# Epsilon greedy policy with epsilon annealing
if not cfg.evaluate and steps * cfg.frame_skip < cfg.eps_anneal_over:
# Only compute epsilon step while we're still annealing epsilon
epsilon = cfg.eps_initial - steps * (
(cfg.eps_initial - cfg.eps_final) / cfg.eps_anneal_over)
else:
epsilon = cfg.eps_final
# Epsilon greedy policy
if np.random.uniform() < epsilon:
action = np.random.randint(0, cfg.num_actions)
else:
action = sess.run(dqn.action, feed_dict={dqn.S: [env.state]})
# Perform environment step
steps = sess.run(increment_step)
reward, next_state, terminal = env.act(action)
if not cfg.evaluate:
replay_buffer.append(next_state[:, :, -1, np.newaxis], action,
reward, terminal)
# Sample and do gradient updates
if steps % cfg.learning_freq == 0:
placeholders = [
dqn.S,
dqn.actions,
dqn.rewards,
dqn.S_p,
dqn.terminals,
]
batch = replay_buffer.sample(cfg.batch_size)
train_op = [dqn.train]
if steps % (cfg.learning_freq * cfg.model_freq) == 0:
experience_batch = batch
batch = imagined_batch(vaes, batch[1])
if steps / (cfg.learning_freq * cfg.model_freq) < 10:
placeholders.append(image_summaries_ph)
batch = list(batch)
batch.append(batch[0][
np.random.randint(0, 32, size=4), :, :, :])
train_op.extend(image_summaries)
if steps % cfg.log_summary_every:
train_op.append(dqn.summary)
result = sess.run(
train_op,
feed_dict=dict(zip(placeholders, batch)),
)
if len(result) > 1:
for i in range(1, len(result)):
summary_writer.add_summary(result[i],
global_step=steps)
if steps % cfg.target_update_every == 0:
sess.run([dqn.copy_to_target])
if steps % cfg.model_chkpt_every == 0:
saver.save(sess,
"%s/model_epoch_%04d" % (cfg.save_dir, steps))
if terminal:
episode_results.write("%d,%d,%d\n" %
(env.episode_count, env.episode_reward,
env.episode_frames))
episode_results.flush()
# Log episode summaries to Tensorboard
add_simple_summary(summary_writer, "episode/reward",
env.episode_reward, env.episode_count)
add_simple_summary(summary_writer, "episode/frames",
env.episode_frames, env.episode_count)
pbar.update(env.episode_frames if not cfg.evaluate else 1)
env.reset()
episode_results.close()
tf.logging.info("Finished %d %s" % (
cfg.max_episode_count if cfg.evaluate else cfg.num_frames,
"episodes" if cfg.evaluate else "frames",
))
parser.add_argument("--target-model-update-freq", type=int, default=10000, help="how often (in steps) to update the target model. Note nature paper says this is in 'number of parameter updates' but their code says steps. see tinyurl.com/hokp4y8")
parser.add_argument("--model", help="tensorflow model checkpoint file to initialize from")
parser.add_argument("rom", help="rom file to run")
args = parser.parse_args()
print('Arguments: %s' % (args))
game_name = os.path.splitext(os.path.split(args.rom)[1])[0]
baseOutputDir = 'out-'+ game_name + '-' + time.strftime("%Y-%m-%d-%H-%M-%S")
os.makedirs(baseOutputDir)
logging.info("Training game "+game_name)
logging.info("Storing training into "+baseOutputDir)
State.setup(args)
environment = AtariEnvironment(args, baseOutputDir)
dqn_network = dqn.DeepQNetwork(environment.getNumActions(), baseOutputDir, args)
replayMemory = replay.ReplayMemory(args)
def runEpoch(minEpochSteps, evalWithEpsilon=None):
logging.info('Running epoch with min epoch steps: %d' % minEpochSteps)
stepStart = environment.getStepNumber()
isTraining = True if evalWithEpsilon is None else False
startGameNumber = environment.getGameNumber()
epochTotalScore = 0
pbar = tqdm(total=minEpochSteps)
while environment.getStepNumber() - stepStart < minEpochSteps:
startTime = lastLogTime = time.time()
def __init__(self, args):
super(ActorLearner, self).__init__()
self.summ_base_dir = args.summ_base_dir
self.local_step = 0
self.global_step = args.global_step
self.actor_id = args.actor_id
self.alg_type = args.alg_type
self.max_local_steps = args.max_local_steps
self.optimizer_type = args.opt_type
self.optimizer_mode = args.opt_mode
self.num_actions = args.num_actions
self.initial_lr = args.initial_lr
self.lr_annealing_steps = args.lr_annealing_steps
# Shared mem vars
self.learning_vars = args.learning_vars
size = self.learning_vars.size
self.flat_grads = np.empty(size, dtype=ctypes.c_float)
if (self.optimizer_mode == "local"):
if (self.optimizer_type == "rmsprop"):
self.opt_st = np.ones(size, dtype=ctypes.c_float)
else:
self.opt_st = np.zeros(size, dtype=ctypes.c_float)
elif (self.optimizer_mode == "shared"):
self.opt_st = args.opt_state
# rmsprop/momentum
self.alpha = args.alpha
# adam
self.b1 = args.b1
self.b2 = args.b2
self.e = args.e
if args.env == "GYM":
from atari_environment import AtariEnvironment
self.emulator = AtariEnvironment(args.game, args.visualize)
else:
from emulator import Emulator
self.emulator = Emulator(args.rom_path, args.game, args.visualize,
self.actor_id, args.random_seed,
args.single_life_episodes)
self.grads_update_steps = args.grads_update_steps
self.max_global_steps = args.max_global_steps
self.gamma = args.gamma
# Exploration epsilons
self.epsilon = 1.0
self.initial_epsilon = 1.0
self.final_epsilon = generate_final_epsilon()
self.epsilon_annealing_steps = args.epsilon_annealing_steps
self.rescale_rewards = args.rescale_rewards
self.max_achieved_reward = -1000000
if self.rescale_rewards:
self.thread_max_reward = 1.0
# Barrier to synchronize all actors after initialization is done
self.barrier = args.barrier
self.summary_ph, self.update_ops, self.summary_ops = self.setup_summaries(
)
self.game = args.game
def actor_learner_thread(num, env, session, graph_ops, summary_ops, saver):
# We use global shared counter T, and TMAX constant
global TMAX, T
# Unpack graph ops
s, a, R, minimize, p_network, v_network = graph_ops
# Unpack tensorboard summary stuff
r_summary_placeholder, update_ep_reward, val_summary_placeholder, update_ep_val, summary_op = summary_ops
# Wrap env with AtariEnvironment helper class
env = AtariEnvironment(gym_env=env,
resized_width=RESIZED_WIDTH,
resized_height=RESIZED_HEIGHT,
agent_history_length=AGENT_HISTORY_LENGTH)
time.sleep(5 * num)
# Set up per-episode counters
ep_reward = 0
ep_avg_v = 0
v_steps = 0
ep_t = 0
probs_summary_t = 0
s_t = env.get_initial_state()
terminal = False
while T < TMAX:
s_batch = []
past_rewards = []
a_batch = []
t = 0
t_start = t
while not (terminal or ((t - t_start) == t_max)):
# Perform action a_t according to policy pi(a_t | s_t)
probs = session.run(p_network, feed_dict={s: [s_t]})[0]
action_index = sample_policy_action(ACTIONS, probs)
a_t = np.zeros([ACTIONS])
a_t[action_index] = 1
if probs_summary_t % 100 == 0:
print "P, ", np.max(probs), "V ", session.run(
v_network, feed_dict={s: [s_t]})[0][0]
s_batch.append(s_t)
a_batch.append(a_t)
s_t1, r_t, terminal, info = env.step(action_index)
ep_reward += r_t
r_t = np.clip(r_t, -1, 1)
past_rewards.append(r_t)
t += 1
T += 1
ep_t += 1
probs_summary_t += 1
s_t = s_t1
if terminal:
R_t = 0
else:
R_t = session.run(v_network,
feed_dict={s: [s_t]
})[0][0] # Bootstrap from last state
R_batch = np.zeros(t)
for i in reversed(range(t_start, t)):
R_t = past_rewards[i] + GAMMA * R_t
R_batch[i] = R_t
session.run(minimize, feed_dict={R: R_batch, a: a_batch, s: s_batch})
# Save progress every 5000 iterations
if T % CHECKPOINT_INTERVAL == 0:
saver.save(session, CHECKPOINT_SAVE_PATH, global_step=T)
if terminal:
# Episode ended, collect stats and reset game
session.run(update_ep_reward,
feed_dict={r_summary_placeholder: ep_reward})
print "THREAD:", num, "/ TIME", T, "/ REWARD", ep_reward
s_t = env.get_initial_state()
terminal = False
# Reset per-episode counters
ep_reward = 0
ep_t = 0
parser.add_argument("--model",
help="tensorflow model checkpoint file to initialize from")
parser.add_argument("rom", help="rom file to run")
args = parser.parse_args()
print('Arguments: %s' % (args))
game_name = os.path.splitext(os.path.split(args.rom)[1])[0]
baseOutputDir = 'out-' + game_name + '-' + time.strftime("%Y-%m-%d-%H-%M-%S")
os.makedirs(baseOutputDir)
logging.info("Training game " + game_name)
logging.info("Storing training into " + baseOutputDir)
State.setup(args)
environment = AtariEnvironment(args, baseOutputDir)
dqn_network = dqn.DeepQNetwork(environment.getNumActions(), baseOutputDir,
args)
replayMemory = replay.ReplayMemory(args)
def runEpoch(minEpochSteps, evalWithEpsilon=None):
logging.info('Running epoch with min epoch steps: %d' % minEpochSteps)
stepStart = environment.getStepNumber()
isTraining = True if evalWithEpsilon is None else False
startGameNumber = environment.getGameNumber()
epochTotalScore = 0
pbar = tqdm(total=minEpochSteps)