def __init__(self,
game,
name,
s_size,
a_size,
number_of_agents,
trainer,
model_path,
global_episodes,
amount_of_agents_to_send_message_to,
display=False,
comm=False,
comm_size_per_agent=0,
spread_messages=True,
comm_delivery_failure_chance=0,
comm_gaussian_noise=0,
comm_jumble_chance=0):
self.name = "worker_" + str(name)
self.is_chief = self.name == 'worker_0'
print(self.name)
self.number = name
self.number_of_agents = number_of_agents
self.model_path = model_path
self.trainer = trainer
self.global_episodes = global_episodes
self.amount_of_agents_to_send_message_to = amount_of_agents_to_send_message_to
self.episode_rewards = []
self.episode_lengths = []
self.episode_mean_values = []
with tf.variable_scope(self.name):
self.increment = self.global_episodes.assign_add(1)
self.summary_writer = tf.summary.FileWriter("train_" +
str(self.number))
# Create the local copy of the network and the tensorflow op to copy global parameters to local network
self.local_AC = AC_Network(
s_size, a_size,
amount_of_agents_to_send_message_to * comm_size_per_agent,
amount_of_agents_to_send_message_to *
comm_size_per_agent if spread_messages else comm_size_per_agent,
self.name, trainer)
self.update_local_ops = update_target_graph('global', self.name)
# Env Pursuit set-up
self.env = game
self.s_size = s_size
self.comm = comm
self.display = display
self.message_size = comm_size_per_agent
self.spread_messages = spread_messages
self.spread_rewards = False
self.comm_delivery_failure_chance = comm_delivery_failure_chance
self.comm_gaussian_noise = comm_gaussian_noise
self.comm_jumble_chance = comm_jumble_chance
def __init__(self, game, name, s_size, a_size, trainer, model_path, global_episodes):
self.name = "worker_" + str(name)
self.number = name
self.model_path = model_path
self.trainer = trainer
self.global_episodes = global_episodes
self.increment = self.global_episodes.assign_add(1)
self.episode_rewards = []
self.episode_lengths = []
self.episode_mean_values = []
self.summary_writer = tf.summary.FileWriter("train_" + str(self.number))
# TODO Each worker: Select random track, select random car.
# TODO Screen: Resolution, colour? Capture screens.
# TODO Features: Render game features? Mini-map, speedometer? Camera-angle?
# TODO Looping: How and when to timeout? How to restart (when off track, when going backwards etc.)?
# TODO Rewards: How given?
# TODO Interact: Do action (steering/speed).
# TODO Interface:
# Constructor.
# env.new_episode()
# env.get_state().screen_buffer
# env.is_episode_finished()
# env.make_action(self.actions[a]) / 100.0
# Create the local copy of the network and the tensorflow op to copy global paramters to local network
self.local_AC = A3CNetwork(s_size, a_size, self.name, trainer)
self.update_local_ops = update_target_graph('global', self.name)
# # The Below code is related to setting up the Doom environment
# game.set_doom_scenario_path("basic.wad") # This corresponds to the simple task we will pose our agent
# game.set_doom_map("map01")
# game.set_screen_resolution(ScreenResolution.RES_160X120)
# #game.set_screen_format(ScreenFormat.GRAY8)
# game.set_render_hud(False)
# game.set_render_crosshair(False)
# game.set_render_weapon(True)
# game.set_render_decals(False)
# game.set_render_particles(False)
# game.add_available_button(Button.MOVE_LEFT)
# game.add_available_button(Button.MOVE_RIGHT)
# game.add_available_button(Button.ATTACK)
# game.add_available_game_variable(GameVariable.AMMO2)
# game.add_available_game_variable(GameVariable.POSITION_X)
# game.add_available_game_variable(GameVariable.POSITION_Y)
# game.set_episode_timeout(300)
# game.set_episode_start_time(10)
# game.set_window_visible(False)
# game.set_sound_enabled(False)
# game.set_living_reward(-1)
# game.set_mode(Mode.PLAYER)
# game.init()
self.actions = self.actions = np.identity(a_size, dtype=bool).tolist()
# End Doom set-up
self.env = game
def __init__(self, game, name, s_size, s_size_central, a_size, number_of_agents, trainer, model_path,
global_episodes,
display=False, comm=False, comm_size_per_agent=0, spread_messages=True,
critic_action=False, critic_comm=False,
comm_delivery_failure_chance=0, comm_gaussian_noise=0, comm_jumble_chance=0):
self.name = "worker_" + str(name)
self.is_chief = self.name == 'worker_0'
print(self.name)
self.number = name
self.number_of_agents = number_of_agents
self.model_path = model_path
self.trainer = trainer
self.global_episodes = global_episodes
self.episode_rewards = []
self.episode_lengths = []
self.episode_mean_values = []
with tf.variable_scope(self.name):
self.increment = self.global_episodes.assign_add(1)
self.summary_writer = tf.summary.FileWriter("train_" + str(self.number))
self.critic_action = critic_action
self.critic_comm = critic_comm
# Create the local copy of the network and the tensorflow op to copy global parameters to local network
self.local_AC = \
[AC_Network(s_size[i], s_size_central[i], number_of_agents, a_size[i],
(number_of_agents - 1) * comm_size_per_agent,
(number_of_agents - 1) * comm_size_per_agent if spread_messages else comm_size_per_agent,
self.name, trainer, "_agent" + str(i), critic_action=critic_action, critic_comm=critic_comm)
for i in range(self.number_of_agents)]
self.update_local_ops = [update_target_graph('global_agent' + str(i), self.name + "_agent" + str(i))
for i in range(self.number_of_agents)]
# Env Pursuit set-up
self.env = game
self.s_size = s_size
self.number_of_actions = a_size
self.action_indexes = [list(range(noa)) for noa in self.number_of_actions]
self.actions_one_hot = [np.zeros([noa, noa]) for noa in self.number_of_actions]
for agent in range(self.number_of_agents):
for i in range(self.number_of_actions[agent]):
self.actions_one_hot[agent][i][i] = 1
self.comm = comm
self.display = display
self.message_size = comm_size_per_agent
self.spread_messages = spread_messages
self.spread_rewards = True
self.comm_delivery_failure_chance = comm_delivery_failure_chance
self.comm_gaussian_noise = comm_gaussian_noise
self.comm_jumble_chance = comm_jumble_chance
def __init__(self, game, name, s_size, a_size, trainer, model_path,
global_episodes):
self.name = "worker_" + str(name)
self.number = name
self.model_path = model_path
self.trainer = trainer
self.global_episodes = global_episodes
self.increment = self.global_episodes.assign_add(1)
self.episode_rewards = []
self.episode_lengths = []
self.episode_mean_values = []
self.summary_writer = tf.summary.FileWriter("train_" +
str(self.number))
# Create the local copy of the network and the tensorflow op to copy global paramters to local network
self.local_AC = A3CNetwork(s_size, a_size, self.name, trainer)
self.update_local_ops = update_target_graph('global', self.name)
# # The Below code is related to setting up the Doom environment
game.set_doom_scenario_path(
"basic.wad"
) # This corresponds to the simple task we will pose our agent
game.set_doom_map("map01")
game.set_screen_resolution(ScreenResolution.RES_160X120)
game.set_screen_format(ScreenFormat.GRAY8)
game.set_render_hud(False)
game.set_render_crosshair(False)
game.set_render_weapon(True)
game.set_render_decals(False)
game.set_render_particles(False)
game.add_available_button(Button.MOVE_LEFT)
game.add_available_button(Button.MOVE_RIGHT)
game.add_available_button(Button.ATTACK)
game.add_available_game_variable(GameVariable.AMMO2)
game.add_available_game_variable(GameVariable.POSITION_X)
game.add_available_game_variable(GameVariable.POSITION_Y)
game.set_episode_timeout(300)
game.set_episode_start_time(10)
game.set_window_visible(False)
game.set_sound_enabled(False)
game.set_living_reward(-1)
game.set_mode(Mode.PLAYER)
game.init()
self.actions = self.actions = np.identity(a_size, dtype=bool).tolist()
# End Doom set-up
self.env = game