def main():
parser = argparse.ArgumentParser(description="Run Extended Q-Learning with given config")
parser.add_argument("-c",
"--config",
type=str,
metavar="",
required=True,
help="Config file name - file must be available as .json in ./configs")
args = parser.parse_args()
# load config files
with open(os.path.join(".", "configs", args.config), "r") as read_file:
config = json.load(read_file)
env = UnityEnvironment(file_name=os.path.join(*config["general"]["env_path"]))
agent = Agent(config=config)
if config["train"]["run_training"]:
scores = sessions.train(agent, env, config)
helper.plot_scores(scores)
agent.save()
else:
agent.load()
sessions.test(agent, env)
env.close()
o_t_reshaped = np.reshape(o_t, (x_dim, y_dim, 2))
o_tp1_reshaped = np.reshape(o_tp1, (x_dim, y_dim, 2))
agent.update_replay_memory(o_t_reshaped, a_t, r_tp1, o_tp1_reshaped,
episode_done)
ep_reward += r_tp1
if not c.test and episode_done:
tsp_cost = tsp_computer.rl_cost(dqn_env.steps)
if max_reward < ep_reward:
str_out = 'max_reward={} < ep_reward={}'.format(
max_reward, ep_reward)
max_reward = ep_reward
agent.save(global_step=global_step)
log.debug(
'saving graph {} -> steps: \n{}, \nstep_length: {}, rl_cost: {}, reward: {}, str_out={}'
.format(global_step, dqn_env.steps, len(dqn_env.steps),
tsp_cost, ep_reward, str_out))
if len(dqn_env.steps) == len(
tsp_computer.coords.keys()) and best_tsp > tsp_cost:
best_tsp = tsp_cost
with open(os.path.join(save_dir, "path.txt"), "a") as myfile:
myfile.write('iteration {}, rl_cost {}: {}\n'.format(
global_step, tsp_cost, dqn_env.steps))
completed_episodes += 1
episode_reward.append(ep_reward)
episode_length.append(t)
rl_cost.append(tsp_computer.rl_cost(dqn_env.steps))
