def run(agent: DDPG, steps: int, save_dir: str) -> float:
sim_distance_file = DataCSVSaver(os.path.join(save_dir, "distance.txt"), ("step", "distance"))
sim_somite_phase_diffs_file = DataCSVSaver(os.path.join(save_dir, "somite_phase_diffs.txt"), ["step"] + ["phi_{}".format(i) for i in range(config.oscillators)])
sim_gripper_phase_diffs_file = DataCSVSaver(os.path.join(save_dir, "gripper_phase_diffs.txt"), ["step"] + ["phi_{}".format(i) for i in range(config.grippers)])
sim_somite_phases_file = DataCSVSaver(os.path.join(save_dir, "somite_phases.txt"), ["step"] + ["phi_{}".format(i) for i in range(config.oscillators)])
sim_gripper_phases_file = DataCSVSaver(os.path.join(save_dir, "gripper_phases.txt"), ["step"] + ["phi_{}".format(i) for i in range(config.grippers)])
sim_somite_actions_file = DataCSVSaver(os.path.join(save_dir, "somite_actions.txt"), ["step"] + ["action_{}".format(i) for i in range(config.oscillators)])
sim_gripper_actions_file = DataCSVSaver(os.path.join(save_dir, "gripper_actions.txt"), ["step"] + ["action_{}".format(i) for i in range(config.grippers)])
sim_frictions_file = DataCSVSaver(os.path.join(save_dir, "frictions.txt"), ["step"] + ["friction_{}".format(i) for i in range(config.somites)])
sim_tension_file = DataCSVSaver(os.path.join(save_dir, "tensions.txt"), ["step"] + ["tension_{}".format(i) for i in range(config.somites - 2)])
caterpillar = Caterpillar(config.somites, config.oscillators_list, config.grippers_list, config.caterpillar_params)
locomotion_distance = utils.locomotion_distance_logger(caterpillar)
for step in range(steps):
obs, somite_phases, gripper_phases = observe(caterpillar)
actions = agent.act(obs)
feedbacks_somite, feedbacks_gripper = actions[:config.oscillators], actions[config.oscillators:]
caterpillar.step_with_feedbacks(config.params["time_delta"], tuple(feedbacks_somite), tuple(feedbacks_gripper))
frictions, tensions, _, _ = np.split(
obs, [config.somites, config.somites * 2 - 2, config.somites * 2 - 2 + config.oscillators])
sim_distance_file.append_data(step, locomotion_distance(caterpillar))
sim_somite_phase_diffs_file.append_data(step, *utils.phase_diffs(np.array(somite_phases)))
sim_gripper_phase_diffs_file.append_data(step, *utils.phase_diffs(np.array(gripper_phases)))
sim_somite_phases_file.append_data(step, *utils.mod2pi(np.array(somite_phases)))
sim_gripper_phases_file.append_data(step, *utils.mod2pi(np.array(gripper_phases)))
sim_frictions_file.append_data(step, *frictions)
sim_tension_file.append_data(step, *tensions)
sim_somite_actions_file.append_data(step, *feedbacks_somite)
sim_gripper_actions_file.append_data(step, *feedbacks_gripper)
caterpillar.save_simulation("{}/render.json".format(save_dir))
return locomotion_distance(caterpillar)
rbuf,
gamma=args.gamma,
explorer=explorer,
replay_start_size=5000,
target_update_method='soft',
target_update_interval=1,
update_interval=4,
soft_update_tau=1e-2,
n_times_update=1,
phi=phi,
minibatch_size=128 #args.minibatch_size
)
# Cargar el modelo guardado
agent.load("DDPG_last_model_rew8k")
for i in range(args.test_episodes):
obs = env.reset()
done = False
reward_Alan = 0
t = 0
while not done and t < 200:
#env.render()
#agent.load("DDPG_best_model")
action = agent.act(obs)
obs, r, done, x = env.step(action)
reward_Alan += r
t += 1
print('Test episode:', i, 'Reward obtained: ', reward_Alan)
#agent.stop_episode()
