def learnControl(config: Configuration):
params = dict()
for k, v in config.items():
if k in alg_dict[config.algorithm].related_parameters():
params[k] = v
if not os.path.exists(config.save_path):
os.makedirs(config.save_path, exist_ok=True)
#rmsve = np.zeros((task_dict[config.task].num_of_policies(), config.num_steps, config.num_of_runs))
steps = np.zeros((config.num_steps, config.num_of_runs))
for run in range(config.num_of_runs):
random_seed = (run + config.num_of_runs) if config.rerun else run
np.random.seed(random_seed)
env = environment_dict[config.environment]()
task = task_dict[config.task](run_number=run,
num_steps=config.num_steps)
agent = alg_dict[config.algorithm](task, **params)
print()
agent.state = env.reset()
#rmsve_of_run = np.zeros((task.num_policies, task.num_steps))
#error_render = ErrorRender(task.num_policies, task.num_steps)
for episode in range(task.num_steps):
#rmsve_of_run[:, step], error = agent.compute_rmsve()
#if config.render:
# error_render.add_error(error)
agent.action = agent.choose_behavior_action(agent.state)
while True:
agent.next_state, r, is_terminal, info = env.step(agent.action)
agent.next_action = agent.choose_behavior_action(
agent.next_state)
agent.learn(agent.state, agent.next_state, agent.action,
agent.next_action, r, is_terminal)
if is_terminal:
steps[episode, run] = agent.time_step
agent.state = env.reset()
agent.reset()
break
if agent.time_step >= task.STEP_LIMIT:
steps[episode, run] = agent.time_step
agent.state = env.reset()
agent.reset()
print('Step Limit Exceeded!')
break
agent.state = agent.next_state
agent.action = agent.next_action
#if config.render:
#env.render(mode='screen', render_cls=error_render)
#print(np.mean(rmsve_of_run, axis=0))
#rmsve[:, :, run] = rmsve_of_run
steps_of_runs = np.transpose(steps)
save_result(config.save_path, '_steps_mean_over_runs',
np.mean(steps_of_runs, axis=0), params, config.rerun)
save_result(
config.save_path, '_steps_stderr_over_runs',
np.std(steps_of_runs, axis=0, ddof=1) / np.sqrt(config.num_of_runs),
params, config.rerun)
final_steps_mean_over_episodes = np.mean(
steps_of_runs[:, config.num_steps - int(0.01 * config.num_steps) - 1:],
axis=1)
save_result(
config.save_path, '_mean_stderr_final',
np.array([
np.mean(final_steps_mean_over_episodes),
np.std(final_steps_mean_over_episodes, ddof=1) /
np.sqrt(config.num_of_runs)
]), params, config.rerun)
auc_mean_over_steps = np.mean(steps_of_runs, axis=1)
save_result(
config.save_path, '_mean_stderr_auc',
np.array([
np.mean(auc_mean_over_steps),
np.std(auc_mean_over_steps, ddof=1) / np.sqrt(config.num_of_runs)
]), params, config.rerun)
def learn(config: Configuration):
params = dict()
for k, v in config.items():
if k in alg_dict[config.algorithm].related_parameters():
params[k] = v
if not os.path.exists(config.save_path):
os.makedirs(config.save_path, exist_ok=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
steps = np.zeros((config.num_steps, config.num_of_runs))
for run in range(config.num_of_runs):
random_seed = (run + config.num_of_runs) if config.rerun else run
#np.random.seed(random_seed)
task = task_dict[config.task](run_number=run,
num_episodes=config.num_steps,
device=device)
agent = alg_dict[config.algorithm](task, **params)
for episode in range(task.num_episodes):
# Initialize the environment and state
agent.state = task.reset()
agent.reset()
while True:
# Select and perform an action
agent.action = agent.select_action(agent.state)
agent.next_state, r, is_terminal, info = task.step(
agent.action)
agent.learn(r, is_terminal)
if is_terminal:
steps[episode, run] = agent.time_step
print(agent.time_step)
break
if agent.time_step >= task.STEP_LIMIT:
steps[episode, run] = agent.time_step
print('Step Limit Exceeded!')
break
# Move to the next state
agent.state = agent.next_state
#if episode % agent.TARGET_UPDATE == 0:
# agent.update_target()
task.env.close()
steps_of_runs = np.transpose(steps)
save_result(config.save_path, '_steps_mean_over_runs',
np.mean(steps_of_runs, axis=0), params, config.rerun)
save_result(
config.save_path, '_steps_stderr_over_runs',
np.std(steps_of_runs, axis=0, ddof=1) / np.sqrt(config.num_of_runs),
params, config.rerun)
final_steps_mean_over_episodes = np.mean(
steps_of_runs[:, config.num_steps - int(0.01 * config.num_steps) - 1:],
axis=1)
save_result(
config.save_path, '_mean_stderr_final',
np.array([
np.mean(final_steps_mean_over_episodes),
np.std(final_steps_mean_over_episodes, ddof=1) /
np.sqrt(config.num_of_runs)
]), params, config.rerun)
auc_mean_over_steps = np.mean(steps_of_runs, axis=1)
save_result(
config.save_path, '_mean_stderr_auc',
np.array([
np.mean(auc_mean_over_steps),
np.std(auc_mean_over_steps, ddof=1) / np.sqrt(config.num_of_runs)
]), params, config.rerun)
def learn(config: Configuration):
params = dict()
for k, v in config.items():
if k in alg_dict[config.algorithm].related_parameters():
params[k] = v
if not os.path.exists(config.save_path):
os.makedirs(config.save_path, exist_ok=True)
rmsve = np.zeros((task_dict[config.task].num_of_policies(),
config.num_steps, config.num_of_runs))
for run in range(config.num_of_runs):
random_seed = (run + config.num_of_runs) if config.rerun else run
np.random.seed(random_seed)
env = environment_dict[config.environment]()
task = task_dict[config.task](run_number=run,
num_steps=config.num_steps)
agent = alg_dict[config.algorithm](task, **params)
rmsve_of_run = np.zeros((task.num_policies, task.num_steps))
agent.state = env.reset()
error_render = ErrorRender(task.num_policies, task.num_steps)
for step in range(task.num_steps):
rmsve_of_run[:, step], error = agent.compute_rmsve()
if config.render:
error_render.add_error(error)
agent.action = agent.choose_behavior_action()
agent.next_state, r, is_terminal, info = env.step(agent.action)
agent.learn(agent.state, agent.next_state, r, is_terminal)
if is_terminal:
agent.state = env.reset()
agent.reset()
continue
agent.state = agent.next_state
if config.render:
env.render(mode='screen', render_cls=error_render)
print(np.mean(rmsve_of_run, axis=0))
rmsve[:, :, run] = rmsve_of_run
rmsve_of_runs = np.transpose(np.mean(rmsve,
axis=0)) # Average over all policies.
save_result(config.save_path, '_RMSVE_mean_over_runs',
np.mean(rmsve_of_runs, axis=0), params, config.rerun)
save_result(
config.save_path, '_RMSVE_stderr_over_runs',
np.std(rmsve_of_runs, axis=0, ddof=1) / np.sqrt(config.num_of_runs),
params, config.rerun)
final_errors_mean_over_steps = np.mean(
rmsve_of_runs[:, config.num_steps - int(0.01 * config.num_steps) - 1:],
axis=1)
save_result(
config.save_path, '_mean_stderr_final',
np.array([
np.mean(final_errors_mean_over_steps),
np.std(final_errors_mean_over_steps, ddof=1) /
np.sqrt(config.num_of_runs)
]), params, config.rerun)
auc_mean_over_steps = np.mean(rmsve_of_runs, axis=1)
save_result(
config.save_path, '_mean_stderr_auc',
np.array([
np.mean(auc_mean_over_steps),
np.std(auc_mean_over_steps, ddof=1) / np.sqrt(config.num_of_runs)
]), params, config.rerun)
