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))
