Exemplo n.º 1
0
	def __init__(self):
		Humanoid.__init__(self)
		attributes = {
			'connection' : None
		}
		
		for key in attributes.keys():
			self.attributes[key] = attributes[key]
		
		self.addEventHandler(ActorEnteredRoomHandler())
		self.addEventHandler(ReceivedNotificationHandler())
		self.addEventHandler(EntityDescribedSelfHandler())
		self.addEventHandler(ReceivedFeedbackHandler())
		self.addEventHandler(ActorEmotedHandler())
Exemplo n.º 2
0
	def __init__(self, actorJSON):		
		Humanoid.__init__(self, actorJSON)

		self.attributes['connection']	= None
		self.attributes['menus']		= []
		
		self.attributes['menus'].append(RootMenu(self))
		
		if actorJSON != None:
			self.addEventHandlerByNameWithAdjusters('Actor.EventHandlers.Player.ReceivedNotificationHandler', None)
			self.addEventHandlerByNameWithAdjusters('Actor.EventHandlers.Player.ReceivedFeedbackHandler', None)
			self.addEventHandlerByNameWithAdjusters('Actor.EventHandlers.Player.ActorAttemptedDropHandler', None)
			self.addEventHandlerByNameWithAdjusters('Actor.EventHandlers.Player.ItemDroppedHandler', None)
			self.addEventHandlerByNameWithAdjusters('Actor.EventHandlers.Player.ActorInitiatedItemGrabHandler', None)
			self.addEventHandlerByNameWithAdjusters('Actor.EventHandlers.Player.ActorGrabbedItemHandler', None)
			self.addEventHandlerByNameWithAdjusters('Actor.EventHandlers.Player.ActorViewedEquipmentHandler', None)
Exemplo n.º 3
0
    def __init__(self, actorJSON):
        Humanoid.__init__(self, actorJSON)

        self.attributes['connection'] = None
        self.attributes['menus'] = []

        self.attributes['menus'].append(RootMenu(self))

        if actorJSON != None:
            self.addEventHandlerByNameWithAdjusters(
                'Actor.EventHandlers.Player.ReceivedNotificationHandler', None)
            self.addEventHandlerByNameWithAdjusters(
                'Actor.EventHandlers.Player.ReceivedFeedbackHandler', None)
            self.addEventHandlerByNameWithAdjusters(
                'Actor.EventHandlers.Player.ActorAttemptedDropHandler', None)
            self.addEventHandlerByNameWithAdjusters(
                'Actor.EventHandlers.Player.ItemDroppedHandler', None)
            self.addEventHandlerByNameWithAdjusters(
                'Actor.EventHandlers.Player.ActorInitiatedItemGrabHandler',
                None)
            self.addEventHandlerByNameWithAdjusters(
                'Actor.EventHandlers.Player.ActorGrabbedItemHandler', None)
            self.addEventHandlerByNameWithAdjusters(
                'Actor.EventHandlers.Player.ActorViewedEquipmentHandler', None)
def main():
    best_reward = 16.0
    # writing rewards in the csv file

    file = open(reward_file, 'a')
    writer = csv.writer(file)

    env = Humanoid()
    env_dim = [STATE_DIM, ACTION_DIM]
    agent = DDPG(env_dim)

    agent.actor_network.load_network(continue_eps)
    agent.critic_network.load_network(continue_eps)

    # main loop
    for episode in range(continue_eps + 1, EPISODES):

        state = env.reset()
        for steps in range(MAX_STEPS_PER_EPS):
            action = agent.noise_action(state)

            next_state, reward, done, _ = env.step(action)
            agent.perceive(state, action, reward, next_state, done)
            # env.unpause()
            state = next_state
            if steps >= MAX_STEPS_PER_EPS - 1:
                done = True
            if done:
                print("Episode " + str(episode) + " : steps count = " +
                      str(steps) + " , reward =" + str(reward))
                break

        # Testing:
        if episode % 50 == 0 and episode != 0:
            traj_file = open(trajectory_file, 'wt')
            traj_writer = csv.writer(traj_file, delimiter='\t')
            traj_writer.writerow([
                'gx', 'gy', 'gz', 'vx', 'vy', 'vz', 'wx', 'wy', 'wz', 'q 9',
                'q 10', 'q 11', 'q 12', 'q 13', 'q 14', 'q 15', 'q 16', 'q 17',
                'q 18', 'qd 9', 'qd 10', 'qd 11', 'qd 12', 'qd 13', 'qd 14',
                'qd 15', 'qd 16', 'qd 17', 'qd 18', 'tc1', 'tc2', 'duration'
            ])

            print("testing...")
            total_reward = 0
            count_of_1 = 0
            agent.actor_network.save_network(episode)
            agent.critic_network.save_network(episode)

            for i in range(TEST):

                state = env.reset()
                for steps in range(MAX_STEPS_PER_EPS):
                    action = agent.action(state)  # direct action for test
                    next_state, reward, done, _ = env.step(action)
                    traj_writer.writerow(state)

                    traj_file.flush()

                    # todo : find what this for
                    # if reward == 1:
                    #     count_of_1 += 1
                    total_reward += reward
                    if steps >= MAX_STEPS_PER_EPS - 1:
                        done = True
                    if done:
                        # print("Episode TEST finished !")
                        break

            ave_reward = total_reward / TEST

            # env.latest_reward = ave_reward
            if ave_reward > best_reward:
                best_reward = ave_reward

            # env.avg_reward = ave_reward
            writer.writerow([ave_reward])
            file.flush()

            print("episode: ", episode, "Evaluation Average Reward: ",
                  ave_reward)
            print("best_reward: ", best_reward)

    pass