def train_caterpillar(save_dir: utils.SaveDir, actor_module_name: str): # Dump train parameters config.print_config() actor_module = import_module(actor_module_name) actor_class = getattr(actor_module, config.COMMON_ACTOR_NAME) rlm = PEPGActorManager(actor_class) caterpillar = Caterpillar(config.somites, mode="default") config.dump_config(save_dir.log_dir()) distance_log = DataCSVSaver(os.path.join(save_dir.log_dir(), "distance.txt"), ("episode", "distance")) reward_log = DataCSVSaver(os.path.join(save_dir.log_dir(), "reward.txt"), ("episode", "reward")) sigma_log = DataCSVSaver(os.path.join(save_dir.log_dir(), "sigma.txt"), ("episode", "average sigma")) episode = 0 while episode < config.params["episodes"]: params_sets = rlm.sample_params() print("\nEpisode: {}".format(episode)) print("---------------------------------------------------------------------") rewards = [] try: with Pool(processes=config.exec_params["worker_processes"], initializer=mute) as pool: rewards = pool.map(run_simulation, [(config.params["steps"], actor_module_name, p_set, []) for p_set in params_sets]) rlm.update_params(np.array(rewards)) episode += 1 # Try parameters after this episode -------------------------------------- rlm.set_params(rlm.parameters) caterpillar.reset() (accumulated_tension,) = exec_steps(config.params["steps"], rlm.get_actor(), caterpillar, [], episode=episode - 1) reward = caterpillar.moved_distance() - accumulated_tension / config.params["tension_divisor"] # Save parameter performance distance_log.append_data(episode, caterpillar.moved_distance()) sigma_log.append_data(episode, np.mean(rlm.sigmas)) reward_log.append_data(episode, reward) announce = " --- Distance: {} Reward: {}".format(caterpillar.moved_distance(), reward) print(announce) except KeyboardInterrupt: command = input("\nSample? Finish? : ") if command in ["sample", "Sample"]: rlm.set_params(rlm.parameters) test_current_params(rlm.get_actor(), save_dir.log_dir(), episode) continue if command in ["finish", "Finish"]: print("Ending training ...") break rlm.set_params(rlm.parameters) rlm.save(save_file_path=os.path.join(save_dir.model_dir(), 'actor_model.pickle')) return rlm.get_actor()
def run_caterpillar(actor, save_dir: str, steps: int, disable_list=None, broken_value=0): if disable_list is None: disable_list = [] caterpillar = Caterpillar(config.somites, mode="default") # Record during sampling sim_distance_file = DataCSVSaver(os.path.join(save_dir, "distance.txt"), ("step", "distance")) sim_phase_diffs_file = DataCSVSaver( os.path.join(save_dir, "phase_diffs.txt"), ["step"] + ["phi_{}".format(i) for i in range(config.oscillators)]) sim_phases_file = DataCSVSaver( os.path.join(save_dir, "phases.txt"), ["step"] + ["phi_{}".format(i) for i in range(config.oscillators)]) sim_actions_file = DataCSVSaver( os.path.join(save_dir, "actions.txt"), ["step"] + ["action_{}".format(i) for i in range(config.oscillators)]) 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.oscillators)]) for step in range(steps): obv, action = observe_and_act(actor, caterpillar, disable_list=disable_list, broken_value=broken_value) _, frictions, tensions = obv caterpillar.step(step, int(1 / config.params["time_dalte"] / 10)) # Save data sim_distance_file.append_data(step, caterpillar.moved_distance()) sim_phase_diffs_file.append_data(step, *caterpillar.phases_from_base()) sim_phases_file.append_data(step, *caterpillar.phis) sim_actions_file.append_data(step, *action) sim_frictions_file.append_data(step, *frictions) sim_tension_file.append_data(step, *tensions) caterpillar.save_simulation("{}/render.sim".format(save_dir)) return caterpillar.moved_distance()
def run_simulation(sim_vals) -> float: """ Run caterpillar with a policy given in argument. This function is for multiprocessing. sim_vals: ( steps: int, actor_module_name: str, actor_params: [np.array(params_0), np.array(params_1), ...], disable_list: list ) """ steps, actor_module_name, actor_params, disable_list = sim_vals assert isinstance(steps, int) assert isinstance(actor_params, Iterable) assert isinstance(disable_list, Iterable) # Init actor actor_module = import_module(actor_module_name) actor = getattr(actor_module, config.COMMON_ACTOR_NAME)() actor.set_params(actor_params) # Init caterpillar caterpillar = Caterpillar(config.somites, mode="default") # Run steps (accumulated_tension,) = exec_steps(steps, actor, caterpillar, disable_list=disable_list) reward = caterpillar.moved_distance() - accumulated_tension / config.params["tension_divisor"] return reward
def test_current_params(actor: base_actor, log_dir: str, episode: int): steps = input("How many steps for this sample?: ") if steps == "": utils.notice("default steps {}".format(config.params["default_sample_steps"])) steps = config.params["default_sample_steps"] # Record during sampling sim_distance_file = DataCSVSaver( "{}/train_result_ep{}_distance.txt".format(log_dir, episode), ("step", "distance") ) sim_phase_diffs_file = DataCSVSaver( "{}/train_result_ep{}_phase_diffs.txt".format(log_dir, episode), ["step"] + ["phi_{}".format(i) for i in range(config.oscillators)] ) sim_actions_file = DataCSVSaver( "{}/train_result_ep{}_actions.txt".format(log_dir, episode), ["step"] + ["action_{}".format(i) for i in range(config.oscillators)] ) sim_frictions_file = DataCSVSaver( "{}/train_result_ep{}_frictions.txt".format(log_dir, episode), ["step"] + ["friction_{}".format(i) for i in range(config.somites)] ) caterpillar = Caterpillar(config.somites, mode="default") for step in range(int(steps)): try: _, action = caterpillar_runner.observe_and_act(actor, caterpillar, []) caterpillar.step(step, int(1 / config.params["time_dalte"] / 10)) except Exception: continue else: # Save data sim_distance_file.append_data(step, caterpillar.moved_distance()) sim_phase_diffs_file.append_data(step, *caterpillar.phases_from_base()) sim_actions_file.append_data(step, *action) frictions = caterpillar.frictions() sim_frictions_file.append_data(step, *frictions) print("Moved distance:", caterpillar.moved_distance()) caterpillar.save_simulation("{}/train_result_ep{}.sim".format(log_dir, episode))