def load(self, env_name):
file_name = self.params['load_dir'] + "DQL_" + env_name + ".ptm"
agent_state = torch.load(file_name,
map_location=lambda storage, loc: storage)
self.Q.load_state_dict(agent_state["Q"])
self.Q.to(device)
self.best_mean_reward = agent_state["best_mean_reward"]
self.best_reward = agent_state["best_reward"]
print("Cargado del modelo Q desde", file_name,
"que hasta el momento tiene una mejor recompensa media de: ",
self.best_mean_reward, " y una recompensa máxima de: ",
self.best_reward)
if __name__ == "__main__":
env_conf = manager.get_environment_params()
env_conf["env_name"] = args.env
if args.test:
env_conf["episodic_life"] = False
reward_type = "LIFE" if env_conf["episodic_life"] else "GAME"
custom_region_available = False
for key, value in env_conf["useful_region"].items():
if key in args.env:
env_conf["useful_region"] = value
custom_region_available = True
break
if custom_region_available is not True:
env_conf["useful_region"] = env_conf["useful_region"]["Default"]
print("Configuración a utilizar:", env_conf)
writer.add_scalar(self.actor_name + "/reward", reward,
self.global_step_num)
writer.add_scalar(self.actor_name + "/ep_reward", ep_reward,
self.global_step_num)
writer.add_scalar(self.actor_name + "/mean_ep_reward",
np.mean(episode_rewards),
self.global_step_num)
writer.add_scalar(self.actor_name + "/max_ep_reward",
self.best_reward, self.global_step_num)
if __name__ == "__main__":
agent_params = manager.get_agent_params()
agent_params["model_dir"] = args.output_dir
agent_params["test"] = args.test
env_params = manager.get_environment_params()
env_params["env_name"] = args.env
mp.set_start_method("spawn")
agent_procs = [
DeepActorCriticAgent(id, args.env, agent_params, env_params)
for id in range(agent_params["num_agents"])
]
[p.start() for p in agent_procs]
[p.join() for p in agent_procs]
