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 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, 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, 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 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 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 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 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
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])
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])
