def __init__(self, sess, gui, displayer, saver):
"""
Build a new instance of Environment, QNetwork and ExperienceBuffer.
Args:
sess : the tensorflow session in which to build the network
gui : a GUI instance to manage the control of the agent
displayer: a Displayer instance to keep track of the episode rewards
saver : a Saver instance to save periodically the network
"""
print("Initializing the agent...")
self.sess = sess
self.gui = gui
self.displayer = displayer
self.saver = saver
self.env = Environment()
self.network = Network(sess)
self.buffer = ExperienceBuffer()
self.best_run = -1e10
self.n_gif = 0
print("Agent initialized !")
def __init__(self, game, headless=True):
if headless:
logging.info("Running in headless mode")
self.actions = [
self.build_ship, self.move_south, self.move_west, self.move_north,
self.move_east, self.stay_still
]
self.network = Network(headless, len(self.actions))
self.observer = Observer(game, headless)
self.reward_map = {
self.build_ship: [0, -10],
self.move_south: [0, -10],
self.move_west: [0, -10],
self.move_north: [0, -10],
self.move_east: [0, -10],
self.stay_still: [1, -10]
}
self.network.draw()
self.observer.new_observation()
self.observer.draw(game)
self.state = self.observer.show()
self.command_queue = []
def __init__(self, dispatch):
self.dispatch = dispatch
self.reset()
self.solver = Network(OBSERVATION_SPACE, len(self.actions) - 1)
self.state_last = agent.AgentState()
self.state_last_last = agent.AgentState()
self.init()
self.solver.load_model()
self.lastRenderingTime = time.perf_counter()
self.episodes = 0
self.action_memory = []
self.state_memory = []
self.reward_memory = []
def __init__(self, sess):
print("Initializing the agent...")
self.sess = sess
self.env = Environment()
self.state_size = self.env.get_state_size()[0]
self.action_size = self.env.get_action_size()
self.low_bound, self.high_bound = self.env.get_bounds()
self.buffer = ExperienceBuffer()
print("Creation of the actor-critic network")
self.network = Network(self.state_size, self.action_size,
self.low_bound, self.high_bound)
self.sess.run(tf.global_variables_initializer())
DISPLAYER.reset()
def __init__(self, sess):
print("Initializing the agent...")
self.sess = sess
self.env = Environment()
self.state_size = self.env.get_state_size()[0]
self.action_size = self.env.get_action_size()
self.bounds = self.env.get_bounds()
print("Creation of the actor-critic network")
self.network = Network(self.sess, self.state_size, self.action_size,
self.bounds)
self.critic_lr = settings.CRITIC_LEARNING_RATE
self.actor_lr = settings.ACTOR_LEARNING_RATE
self.delta_critic_lr = self.critic_lr / settings.TRAINING_EPS
self.delta_actor_lr = self.actor_lr / settings.TRAINING_EPS
self.sess.run(tf.global_variables_initializer())
def __init__(self, sess):
print("Initializing the agent...")
self.sess = sess
self.env = Environment()
self.state_size = self.env.get_state_size()[0]
self.action_size = self.env.get_action_size()
self.low_bound, self.high_bound = self.env.get_bounds()
self.buffer = PrioritizedReplayBuffer(parameters.BUFFER_SIZE,
parameters.ALPHA)
print("Creation of the actor-critic network...")
self.network = Network(self.state_size, self.action_size,
self.low_bound, self.high_bound)
print("Network created !\n")
self.epsilon = parameters.EPSILON_START
self.beta = parameters.BETA_START
self.best_run = -1e10
self.sess.run(tf.global_variables_initializer())
def __init__(self, sess):
print("Initializing the agent...")
self.sess = sess
self.env = Environment()
self.state_size = self.env.get_state_size()[0]
self.action_size = self.env.get_action_size()
self.low_bound, self.high_bound = self.env.get_bounds()
self.buffer = ExperienceBuffer()
print("Creation of the actor-critic network")
self.network = Network(self.state_size, self.action_size,
self.low_bound, self.high_bound)
self.epsilon = parameters.EPSILON_START
self.epsilon_decay = (parameters.EPSILON_START -
parameters.EPSILON_STOP) \
/ parameters.EPSILON_STEPS
self.best_run = -1e10
self.n_gif = 0
self.sess.run(tf.global_variables_initializer())