def work(self,
max_episode_length,
gamma,
sess,
coord=None,
saver=None,
max_episodes=None,
batch_size=25):
episode_count = sess.run(self.global_episodes)
total_steps = 0
print("Starting worker " + str(self.number))
# with sess.as_default(), sess.graph.as_default():
prev_clock = time()
if coord is None:
coord = sess
already_calculated_actions = False
stats1, stats2 = [], []
while not coord.should_stop():
sess.run(self.update_local_ops)
episode_buffer = [[] for _ in range(self.number_of_agents)]
episode_comm_maps = [[] for _ in range(self.number_of_agents)]
episode_values = [[] for _ in range(self.number_of_agents)]
episode_reward = 0
episode_step_count = 0
v_l, p_l, e_l, g_n, v_n = get_empty_loss_arrays(
self.number_of_agents)
partial_obs = [None for _ in range(self.number_of_agents)]
partial_mess_rec = [None for _ in range(self.number_of_agents)]
sent_message = [None for _ in range(self.number_of_agents)]
mgrad_per_received = [None for _ in range(self.number_of_agents)]
# start new epi
current_screen = self.env.reset()
# print(current_screen)
for i in range(self.number_of_agents):
if self.s_size == 6:
current_screen[i] = np.hstack([
current_screen[i][0:4],
current_screen[i][4 + i * 2:6 + i * 2]
])
else:
current_screen[i] = current_screen[i][0:self.s_size]
current_screen_central = np.hstack(current_screen)
arrayed_current_screen_central = [
current_screen_central for _ in range(self.number_of_agents)
]
for i in range(self.number_of_agents):
comm_map = list(range(self.number_of_agents))
comm_map.remove(i)
episode_comm_maps[i].append(comm_map)
if self.is_chief and self.display:
self.env.render()
curr_comm = [[] for _ in range(self.number_of_agents)]
for curr_agent in range(self.number_of_agents):
for from_agent in range(self.number_of_agents - 1):
curr_comm[curr_agent].extend([0] * self.message_size)
stats1_temp = 0
for episode_step_count in range(max_episode_length):
# feedforward pass
if not already_calculated_actions:
action_distribution, message = sess.run(
[self.local_AC.policy, self.local_AC.message],
feed_dict={
self.local_AC.inputs: current_screen,
self.local_AC.inputs_comm: curr_comm
})
actions = [
np.random.choice(self.action_indexes,
p=act_distribution)
for act_distribution in action_distribution
]
actions_one_hot = [
self.actions_one_hot[act] for act in actions
]
# message gauss noise
if self.comm_gaussian_noise != 0:
for index in range(len(message)):
message[index] += np.random.normal(
0, self.comm_gaussian_noise)
if self.critic_action:
for agent in range(self.number_of_agents):
actions_one_hot = one_hot_encoding(
actions[0:agent] + actions[agent + 1:],
self.number_of_actions)
arrayed_current_screen_central[
agent] = arrayed_current_screen_central[
agent] + actions_one_hot
if self.critic_comm:
for agent in range(self.number_of_agents):
arrayed_current_screen_central[
agent] = arrayed_current_screen_central[
agent] + curr_comm[agent]
already_calculated_actions = False
value = sess.run(self.local_AC.value,
feed_dict={
self.local_AC.inputs_central:
arrayed_current_screen_central
})
previous_screen = current_screen
arrayed_previous_screen_central = arrayed_current_screen_central
previous_comm = curr_comm
# Watch environment
current_screen, reward, terminal, info = self.env.step(
actions_one_hot)
for info_agent in info['n']:
stats1_temp += (info_agent[1] - 1) / 2
for i in range(self.number_of_agents):
current_screen[i] = current_screen[i][0:self.s_size]
terminal = np.sum(reward) > (
-0.25) * self.number_of_agents or stats1_temp > 5
current_screen_central = np.hstack(current_screen)
arrayed_current_screen_central = [
current_screen_central
for _ in range(self.number_of_agents)
]
this_turns_comm_map = []
for i in range(self.number_of_agents):
# 50% chance of no comms
surviving_comms = list(range(self.number_of_agents))
surviving_comms.remove(i)
for index in range(len(surviving_comms)):
if random.random(
) < self.comm_delivery_failure_chance: # chance of failure comms
surviving_comms[index] = -1
episode_comm_maps[i].append(surviving_comms)
this_turns_comm_map.append(surviving_comms)
curr_comm = self.output_mess_to_input_mess(
message, this_turns_comm_map)
if self.is_chief and self.display:
self.env.render()
sleep(0.2)
episode_reward += sum(
reward) if self.spread_rewards else reward
# jumbles comms
if self.comm_jumble_chance != 0:
for i in range(self.number_of_agents):
joint_comm = [0] * self.message_size
for index in range(len(curr_comm[i])):
joint_comm[
index %
self.message_size] += curr_comm[i][index]
jumble = False
for index in range(len(curr_comm[i])):
if index % self.message_size == 0:
# only jumble messages that got received
jumble = curr_comm[i][
index] != 0 and random.random(
) < self.comm_jumble_chance
if jumble:
curr_comm[i][index] = joint_comm[
index % self.message_size]
for i in range(self.number_of_agents):
episode_buffer[i].append([
previous_screen[i], arrayed_previous_screen_central[i],
previous_comm[i], actions[i], message[i],
reward[i] if self.spread_rewards else reward,
current_screen[i], curr_comm[i], terminal, value[i]
])
episode_values[i].append(np.max(value[i]))
# If the episode hasn't ended, but the experience buffer is full, then we make an update step
# using that experience rollout.
if len(episode_buffer[0]) == batch_size and not terminal and \
episode_step_count < max_episode_length - 1:
# feedforward pass
action_distribution, message = sess.run(
[self.local_AC.policy, self.local_AC.message],
feed_dict={
self.local_AC.inputs: current_screen,
self.local_AC.inputs_comm: curr_comm
})
actions = [
np.random.choice(self.action_indexes,
p=act_distribution)
for act_distribution in action_distribution
]
actions_one_hot = [
self.actions_one_hot[act] for act in actions
]
if self.critic_action:
for agent in range(self.number_of_agents):
actions_one_hot = one_hot_encoding(
actions[0:agent] + actions[agent + 1:],
self.number_of_actions)
arrayed_current_screen_central[
agent] = arrayed_current_screen_central[
agent] + actions_one_hot
if self.critic_comm:
for agent in range(self.number_of_agents):
arrayed_current_screen_central[
agent] = arrayed_current_screen_central[
agent] + curr_comm[agent]
already_calculated_actions = True
v1 = sess.run(self.local_AC.value,
feed_dict={
self.local_AC.inputs_central:
arrayed_current_screen_central
})
for i in range(self.number_of_agents):
partial_obs[i], partial_mess_rec[i], sent_message[i], mgrad_per_received[i], \
v_l[i], p_l[i], e_l[i], g_n[i], v_n[i] = \
self.train_weights_and_get_comm_gradients(episode_buffer[i], sess, gamma, self.local_AC,
bootstrap_value=v1[i][0])
if self.comm:
mgrad_per_sent = self.input_mloss_to_output_mloss(
batch_size - 1, mgrad_per_received,
episode_comm_maps)
# start a new mini batch with only the last sample in the comm map
temp_episode_comm_maps = []
for i in range(self.number_of_agents):
temp_episode_comm_maps.append(
[episode_comm_maps[i][-1]])
episode_comm_maps = temp_episode_comm_maps
for i in range(self.number_of_agents):
self.apply_comm_gradients(partial_obs[i],
partial_mess_rec[i],
sent_message[i],
mgrad_per_sent[i], sess,
self.local_AC)
# print("Copying global networks to local networks")
sess.run(self.update_local_ops)
# reset episode buffers. keep last value to be used for t_minus_1 message loss
temp_episode_buffer = []
for i in range(self.number_of_agents):
temp_episode_buffer.append([episode_buffer[i][-1]])
episode_buffer = temp_episode_buffer
# Measure time and increase episode step count
total_steps += 1
if total_steps % 2000 == 0:
new_clock = time()
print(2000.0 / (new_clock - prev_clock), "it/s, ")
prev_clock = new_clock
# If both prey and predator have acknowledged game is over, then break from episode
if terminal:
break
stats1.append(stats1_temp)
stats2.append(-reward[0] / self.number_of_agents)
# print("0ver ",episode_step_count,episode_reward)
self.episode_rewards.append(episode_reward)
self.episode_lengths.append(episode_step_count)
self.episode_mean_values.append(np.mean(episode_values))
# Update the network using the experience buffer at the end of the episode.
for i in range(self.number_of_agents):
partial_obs[i], partial_mess_rec[i], sent_message[i], mgrad_per_received[i], \
v_l[i], p_l[i], e_l[i], g_n[i], v_n[i] = \
self.train_weights_and_get_comm_gradients(episode_buffer[i], sess, gamma, self.local_AC)
if self.comm and len(mgrad_per_received[0]) != 0:
mgrad_per_sent = self.input_mloss_to_output_mloss(
len(mgrad_per_received[0]), mgrad_per_received,
episode_comm_maps)
for i in range(self.number_of_agents):
self.apply_comm_gradients(partial_obs[i],
partial_mess_rec[i],
sent_message[i],
mgrad_per_sent[i], sess,
self.local_AC)
# print("Copying global networks to local networks")
sess.run(self.update_local_ops)
# Periodically save gifs of episodes, model parameters, and summary statistics.
if episode_count % 5 == 0:
# Save statistics for TensorBoard
mean_length = np.mean(self.episode_lengths[-5:])
mean_reward = np.mean(self.episode_rewards[-5:])
mean_value = np.mean(self.episode_mean_values[-5:])
if self.is_chief and episode_count % 10 == 0:
print("length", mean_length, "reward", np.max(reward))
# Save current model
if self.is_chief and saver is not None and episode_count % 500 == 0:
saver.save(
sess, self.model_path + '/model-' +
str(episode_count) + '.cptk')
print("Saved Model")
summary = tf.Summary()
summary.value.add(
tag='Perf/Length',
simple_value=float(mean_length)) # avg episode length
summary.value.add(
tag='Perf/Reward',
simple_value=float(mean_reward)) # avg reward
summary.value.add(
tag='Perf/Value', simple_value=float(
mean_value)) # avg episode value_predator
summary.value.add(tag='Losses/Value Loss',
simple_value=float(
np.mean(v_l))) # value_loss
summary.value.add(tag='Losses/Policy Loss',
simple_value=float(
np.mean(p_l))) # policy_loss
summary.value.add(tag='Losses/Entropy',
simple_value=float(np.mean(e_l))) # entropy
summary.value.add(tag='Losses/Grad Norm',
simple_value=float(
np.mean(g_n))) # grad_norms
summary.value.add(tag='Losses/Var Norm',
simple_value=float(
np.mean(v_n))) # var_norms
self.summary_writer.add_summary(summary, episode_count)
self.summary_writer.flush()
# Update episode count
if self.is_chief:
episode_count = sess.run(self.increment)
if episode_count % 50 == 0:
print("Global episodes @", episode_count)
if max_episodes is not None and episode_count > max_episodes:
coord.request_stop()
else:
episode_count = sess.run(self.global_episodes)
self.env.close()
print(stats1)
print(stats2)
print(np.mean(stats1), np.mean(stats2))
def work(self,
max_episode_length,
gamma,
sess,
coord=None,
saver=None,
max_episodes=None,
batch_size=25):
episode_count = sess.run(self.global_episodes)
total_steps = 0
print("Starting worker " + str(self.number))
# with sess.as_default(), sess.graph.as_default():
prev_clock = time()
if coord is None:
coord = sess
"""comms_evol = [[], [], [], [], [], [], [], []]
if self.display and self.is_chief:
mpl.ion()
mpl.pause(0.001)"""
already_calculated_actions = False
while not coord.should_stop():
sess.run(self.update_local_ops)
episode_buffer = [[] for _ in range(self.number_of_agents)]
episode_comm_maps = [[] for _ in range(self.number_of_agents)]
episode_values = [[] for _ in range(self.number_of_agents)]
episode_reward = 0
episode_step_count = 0
action_indexes = list(range(self.env.max_actions))
v_l, p_l, e_l, g_n, v_n = get_empty_loss_arrays(
self.number_of_agents)
partial_obs = [None for _ in range(self.number_of_agents)]
partial_mess_rec = [None for _ in range(self.number_of_agents)]
sent_message = [None for _ in range(self.number_of_agents)]
mgrad_per_received = [None for _ in range(self.number_of_agents)]
# start new epi
current_screen, info = self.env.reset()
arrayed_current_screen_central = info["state_central"]
for i in range(self.number_of_agents):
episode_comm_maps[i].append(current_screen[i][1:4])
# replace state to just show whether cars were there or not, and not which cars
for neighbor_index in range(1, 4):
if current_screen[i][neighbor_index] >= 0:
current_screen[i][neighbor_index] = 1
if self.is_chief and self.display:
self.env.render()
curr_comm = [[] for _ in range(self.number_of_agents)]
for curr_agent in range(self.number_of_agents):
for from_agent in range(
self.amount_of_agents_to_send_message_to):
curr_comm[curr_agent].extend([0] * self.message_size)
for episode_step_count in range(max_episode_length):
# feedforward pass
if not already_calculated_actions:
action_distribution, message = sess.run(
[self.local_AC.policy, self.local_AC.message],
feed_dict={
self.local_AC.inputs: current_screen,
self.local_AC.inputs_comm: curr_comm
})
actions = [
np.random.choice(action_indexes, p=act_distribution)
for act_distribution in action_distribution
]
# message gauss noise
if self.comm_gaussian_noise != 0:
for index in range(len(message)):
message[index] += np.random.normal(
0, self.comm_gaussian_noise)
# TODO
"""message = []
for my_obs in current_screen:
message.append([my_obs[0]])
for index in range(len(actions)):
# intersection, failed message or other guy has priority, lets await
if current_screen[index][3] == 1 and curr_comm[index][2] >= 0 and current_screen[index][0]==-1:
actions[index] = 0
else:
actions[index] = 1"""
"""for index in range(len(actions)):
# intersection, failed message or other guy has priority, lets await
if current_screen[index][3] == 1 and curr_comm[index][10]==0 and current_screen[index][0]!=1:
print("Actions: %4.2f %4.2f"%(action_distribution[index][0],action_distribution[index][1]))"""
if self.critic_action:
for agent in range(self.number_of_agents):
actions_one_hot = one_hot_encoding(
actions[0:agent] + actions[agent + 1:],
self.number_of_actions)
arrayed_current_screen_central[
agent] = arrayed_current_screen_central[
agent] + actions_one_hot
if self.critic_comm:
for agent in range(self.number_of_agents):
arrayed_current_screen_central[
agent] = arrayed_current_screen_central[
agent] + curr_comm[agent]
already_calculated_actions = False
value = sess.run(self.local_AC.value,
feed_dict={
self.local_AC.inputs_central:
arrayed_current_screen_central
})
previous_screen = current_screen
arrayed_previous_screen_central = arrayed_current_screen_central
previous_comm = curr_comm
# Watch environment
current_screen, reward, terminal, info = self.env.step(actions)
# debug
"""if episode_count % 500 == 0 and self.is_chief:
for index in range(len(actions)):
""if previous_screen[index][3] == 1 and previous_comm[index][10] == 0 and previous_screen[index][0] != 1:
print(episode_step_count, "Agent ", index, ", Actions: %4.2f %4.2f" % (
action_distribution[index][0], action_distribution[index][1]), "action:",actions[index], ", Reward=",
reward[index], "mess", previous_comm[index])""
if previous_screen[index][3] == 1 and previous_screen[index][0] != 1:
print(episode_step_count, "Agent ", index, ", Actions: %4.2f %4.2f" % (
action_distribution[index][0], action_distribution[index][1]), "action:",actions[index], ", Reward=",
reward[index], "mess", previous_comm[index])"""
arrayed_current_screen_central = info["state_central"]
this_turns_comm_map = []
for i in range(self.number_of_agents):
# 50% chance of no comms
surviving_comms = current_screen[i][1:4]
for index in range(len(surviving_comms)):
if random.random(
) < self.comm_delivery_failure_chance: # chance of failure comms
surviving_comms[index] = -1
episode_comm_maps[i].append(surviving_comms)
this_turns_comm_map.append(surviving_comms)
# replace state to just show whether cars were there or not, and not which cars
for neighbor_index in range(1, 4):
if current_screen[i][neighbor_index] >= 0:
current_screen[i][neighbor_index] = 1
curr_comm = self.output_mess_to_input_mess(
message, this_turns_comm_map)
# jumbles comms
if self.comm_jumble_chance != 0:
for i in range(self.number_of_agents):
joint_comm = [0] * self.message_size
for index in range(len(curr_comm[i])):
joint_comm[
index %
self.message_size] += curr_comm[i][index]
jumble = False
for index in range(len(curr_comm[i])):
if index % self.message_size == 0:
# only jumble messages that got received
jumble = curr_comm[i][
index] != 0 and random.random(
) < self.comm_jumble_chance
if jumble:
curr_comm[i][index] = joint_comm[
index % self.message_size]
if self.is_chief and self.display:
self.env.render()
sleep(0.2)
episode_reward += sum(
reward) if self.spread_rewards else reward
for i in range(self.number_of_agents):
episode_buffer[i].append([
previous_screen[i], arrayed_previous_screen_central[i],
previous_comm[i], actions[i], message[i],
reward[i] if self.spread_rewards else reward,
current_screen[i], curr_comm[i], terminal, value[i]
])
episode_values[i].append(np.max(value[i]))
# If the episode hasn't ended, but the experience buffer is full, then we make an update step
# using that experience rollout.
if len(episode_buffer[0]) == batch_size and not terminal and \
episode_step_count < max_episode_length - 1:
# feedforward pass
action_distribution, message = sess.run(
[self.local_AC.policy, self.local_AC.message],
feed_dict={
self.local_AC.inputs: current_screen,
self.local_AC.inputs_comm: curr_comm
})
actions = [
np.random.choice(action_indexes, p=act_distribution)
for act_distribution in action_distribution
]
if self.critic_action:
for agent in range(self.number_of_agents):
actions_one_hot = one_hot_encoding(
actions[0:agent] + actions[agent + 1:],
self.number_of_actions)
arrayed_current_screen_central[
agent] = arrayed_current_screen_central[
agent] + actions_one_hot
if self.critic_comm:
for agent in range(self.number_of_agents):
arrayed_current_screen_central[
agent] = arrayed_current_screen_central[
agent] + curr_comm[agent]
already_calculated_actions = True
v1 = sess.run(self.local_AC.value,
feed_dict={
self.local_AC.inputs_central:
arrayed_current_screen_central
})
for i in range(self.number_of_agents):
partial_obs[i], partial_mess_rec[i], sent_message[i], mgrad_per_received[i], \
v_l[i], p_l[i], e_l[i], g_n[i], v_n[i] = \
self.train_weights_and_get_comm_gradients(episode_buffer[i], sess, gamma, self.local_AC,
bootstrap_value=v1[i][0])
if self.comm:
mgrad_per_sent = self.input_mloss_to_output_mloss(
batch_size - 1, mgrad_per_received,
episode_comm_maps)
# start a new mini batch with only the last sample in the comm map
temp_episode_comm_maps = []
for i in range(self.number_of_agents):
temp_episode_comm_maps.append(
[episode_comm_maps[i][-1]])
episode_comm_maps = temp_episode_comm_maps
for i in range(self.number_of_agents):
self.apply_comm_gradients(partial_obs[i],
partial_mess_rec[i],
sent_message[i],
mgrad_per_sent[i], sess,
self.local_AC)
# exit()
# print("Copying global networks to local networks")
sess.run(self.update_local_ops)
# reset episode buffers. keep last value to be used for t_minus_1 message loss
temp_episode_buffer = []
for i in range(self.number_of_agents):
temp_episode_buffer.append([episode_buffer[i][-1]])
episode_buffer = temp_episode_buffer
# Measure time and increase episode step count
total_steps += 1
if total_steps % 2000 == 0:
new_clock = time()
print(2000.0 / (new_clock - prev_clock), "it/s, ")
prev_clock = new_clock
# If both prey and predator have acknowledged game is over, then break from episode
if terminal:
break
# print("0ver ",episode_step_count,episode_reward)
self.episode_rewards.append(episode_reward)
self.episode_lengths.append(episode_step_count)
self.episode_collisions.append(info["collisions"])
self.episode_stalls.append(info["stalls"])
self.episode_mean_values.append(np.mean(episode_values))
# Update the network using the experience buffer at the end of the episode.
for i in range(self.number_of_agents):
partial_obs[i], partial_mess_rec[i], sent_message[i], mgrad_per_received[i], \
v_l[i], p_l[i], e_l[i], g_n[i], v_n[i] = \
self.train_weights_and_get_comm_gradients(episode_buffer[i], sess, gamma, self.local_AC)
if self.comm and len(mgrad_per_received[0]) != 0:
mgrad_per_sent = self.input_mloss_to_output_mloss(
len(mgrad_per_received[0]), mgrad_per_received,
episode_comm_maps)
for i in range(self.number_of_agents):
self.apply_comm_gradients(partial_obs[i],
partial_mess_rec[i],
sent_message[i],
mgrad_per_sent[i], sess,
self.local_AC)
# print("Copying global networks to local networks")
sess.run(self.update_local_ops)
# Periodically save gifs of episodes, model parameters, and summary statistics.
if episode_count % 5 == 0:
# Save statistics for TensorBoard
mean_length = np.mean(self.episode_lengths[-5:])
mean_collisions = np.mean(self.episode_collisions[-5:])
mean_stalls = np.mean(self.episode_stalls[-5:])
mean_reward = np.mean(self.episode_rewards[-5:])
mean_value = np.mean(self.episode_mean_values[-5:])
if self.is_chief and episode_count % 10 == 0:
print("length", mean_length, "reward", mean_reward,
"collisions", mean_collisions, "stalls", mean_stalls)
# Save current model
if self.is_chief and saver is not None and episode_count % 500 == 0:
saver.save(
sess, self.model_path + '/model-' +
str(episode_count) + '.cptk')
print("Saved Model")
summary = tf.Summary()
summary.value.add(
tag='Perf/Length',
simple_value=float(mean_length)) # avg episode length
summary.value.add(
tag='Perf/Reward',
simple_value=float(mean_reward)) # avg reward
summary.value.add(
tag='Perf/Value', simple_value=float(
mean_value)) # avg episode value_predator
summary.value.add(tag='Losses/Value Loss',
simple_value=float(
np.mean(v_l))) # value_loss
summary.value.add(tag='Losses/Policy Loss',
simple_value=float(
np.mean(p_l))) # policy_loss
summary.value.add(tag='Losses/Entropy',
simple_value=float(np.mean(e_l))) # entropy
summary.value.add(tag='Losses/Grad Norm',
simple_value=float(
np.mean(g_n))) # grad_norms
summary.value.add(tag='Losses/Var Norm',
simple_value=float(
np.mean(v_n))) # var_norms
self.summary_writer.add_summary(summary, episode_count)
self.summary_writer.flush()
"""if self.is_chief and self.message_size == 1:
vals = sess.run(self.local_AC.message,
feed_dict={self.local_AC.inputs: [[-1, -1, -1, 1],
[1, -1, -1, 1],
[-1, 1, -1, 1],
[1, 1, -1, 1],
[-1, -1, 1, 1],
[1, -1, 1, 1],
[-1, 1, 1, 1],
[1, 1, 1, 1]
]})
for i in range(8):
comms_evol[i].append(vals[i])
if self.display:
mpl.clf()
for i in range(8):
mpl.plot(comms_evol[i], color="green" if i % 2 == 0 else "red")
mpl.pause(0.001)
if episode_count%50==0:
print(comms_evol)"""
# Update episode count
if self.is_chief:
episode_count = sess.run(self.increment)
if episode_count % 50 == 0:
print("Global episodes @", episode_count)
if max_episodes is not None and episode_count > max_episodes:
coord.request_stop()
else:
episode_count = sess.run(self.global_episodes)
self.env.close()
