def train(config_file_path: str, save_dir: str, use_vime: bool, random_policy: bool, device: str, visualize_interval: int):
conf_d = toml.load(open(config_file_path))
conf = namedtuple('Config', conf_d.keys())(*conf_d.values())
# Check if saving directory is valid
if "test" in save_dir and os.path.exists(save_dir):
shutil.rmtree(save_dir)
if os.path.exists(save_dir):
raise ValueError("Directory {} already exists.".format(save_dir))
# Create save dir
os.makedirs(save_dir)
ckpt_dir = os.path.join(save_dir, 'checkpoints')
os.makedirs(ckpt_dir)
log_dir = os.path.join(save_dir, 'logs')
os.makedirs(log_dir)
# Save config file
shutil.copyfile(config_file_path, os.path.join(save_dir, os.path.basename(config_file_path)))
# Set random variable
np.random.seed(int(time.time()))
torch.manual_seed(int(time.time()))
device = torch.device(device)
if device.type == 'cuda':
torch.cuda.manual_seed(int(time.time()))
# Set up log metrics
metrics = {
'episode': [],
'collected_samples': [],
'reward': [], # cummulated reward
'curiosity_reward': [], # cummulated reward with information gain
'likelihood': [], # likelihood of leanred dynamics model
'D_KL_median': [], 'D_KL_mean': [],
'q1_loss': [], 'policy_loss': [], 'alpha_loss': [], 'alpha': [],
'ELBO': [],
'step': [], 'step_reward': [],
'test_episode': [], 'test_reward': [],
}
# Set up environment
print("----------------------------------------\nTrain in {}\n----------------------------------------".format(conf.environment))
env = gym.make(conf.environment)
if use_vime:
print("Use VIME")
if random_policy:
print("Keep using random policy.")
# Training set up
agent = SAC(env.observation_space, env.action_space, device, **conf.agent)
memory = ReplayBuffer(conf.replay_buffer_capacity, env.observation_space.shape, env.action_space.shape)
vime = VIME(env.observation_space.shape[0], env.action_space.shape[0], device, **conf.vime) if use_vime else None
# Load checkpoint if specified in config
if conf.checkpoint != '':
ckpt = torch.load(conf.checkpoint, map_location=device)
metrics = ckpt['metrics']
agent.load_state_dict(ckpt['agent'])
memory.load_state_dict(ckpt['memory'])
if use_vime:
vime.load_state_dict(ckpt['vime'])
def save_checkpoint():
# Save checkpoint
ckpt = {'metrics': metrics, 'agent': agent.state_dict(), 'memory': memory.state_dict()}
if use_vime:
ckpt['vime'] = vime.state_dict()
path = os.path.join(ckpt_dir, 'checkpoint.pth')
torch.save(ckpt, path)
# Save agent model only
model_ckpt = {'agent': agent.state_dict()}
model_path = os.path.join(ckpt_dir, 'model.pth')
torch.save(model_ckpt, model_path)
# Save metrics only
metrics_ckpt = {'metrics': metrics}
metrics_path = os.path.join(ckpt_dir, 'metrics.pth')
torch.save(metrics_ckpt, metrics_path)
# Train agent
init_episode = 0 if len(metrics['episode']) == 0 else metrics['episode'][-1] + 1
pbar = tqdm.tqdm(range(init_episode, conf.episodes))
reward_moving_avg = None
moving_avg_coef = 0.1
agent_update_count = 0
total_steps = 0
for episode in pbar:
o = env.reset()
rewards, curiosity_rewards = [], []
info_gains = []
log_likelihoods = []
q1_losses, q2_losses, policy_losses, alpha_losses, alphas = [],[],[],[],[]
for t in range(conf.horizon):
if len(memory) < conf.random_sample_num or random_policy:
a = env.action_space.sample()
else:
a = agent.select_action(o, eval=False)
o_next, r, done, _ = env.step(a)
total_steps += 1
metrics['step'].append(total_steps)
metrics['step_reward'].append(r)
done = False if t == env._max_episode_steps - 1 else bool(done) # done should be False if an episode is terminated forcefully
rewards.append(r)
if use_vime and len(memory) >= conf.random_sample_num:
# Calculate curiosity reward in VIME
info_gain, log_likelihood = vime.calc_info_gain(o, a, o_next)
assert not np.isnan(info_gain).any() and not np.isinf(info_gain).any(), "invalid information gain, {}".format(info_gains)
info_gains.append(info_gain)
log_likelihoods.append(log_likelihood)
vime.memorize_episodic_info_gains(info_gain)
r = vime.calc_curiosity_reward(r, info_gain)
curiosity_rewards.append(r)
memory.append(o, a, r, o_next, done)
o = o_next
# Update agent
if len(memory) >= conf.random_sample_num and not random_policy:
for _ in range(conf.agent_update_per_step):
batch_data = memory.sample(conf.agent_update_batch_size)
q1_loss, q2_loss, policy_loss, alpha_loss, alpha = agent.update_parameters(batch_data, agent_update_count)
q1_losses.append(q1_loss)
q2_losses.append(q2_loss)
policy_losses.append(policy_loss)
alpha_losses.append(alpha_loss)
alphas.append(alpha)
agent_update_count += 1
if done:
break
if len(log_likelihoods) == 0:
log_likelihoods.append(-np.inf)
# Display performance
episodic_reward = np.sum(rewards)
reward_moving_avg = episodic_reward if reward_moving_avg is None else (1-moving_avg_coef) * reward_moving_avg + moving_avg_coef * episodic_reward
if use_vime:
pbar.set_description("EPISODE {}, TOTAL STEPS {}, SAMPLES {} --- Steps {}, Curiosity {:.1f}, Rwd {:.1f} (m.avg {:.1f}), Likelihood {:.2E}".format(
episode, memory.step, len(memory), len(rewards), np.sum(curiosity_rewards), episodic_reward, reward_moving_avg, np.mean(np.exp(log_likelihoods))))
else:
pbar.set_description("EPISODE {}, TOTAL STEPS {}, SAMPLES {} --- Steps {}, Rwd {:.1f} (mov avg {:.1f})".format(
episode, memory.step, len(memory), len(rewards), episodic_reward, reward_moving_avg))
# Save episodic metrics
metrics['episode'].append(episode)
metrics['collected_samples'].append(total_steps)
metrics['reward'].append(episodic_reward)
metrics['curiosity_reward'].append(np.sum(curiosity_rewards))
metrics['likelihood'].append(np.mean(np.exp(log_likelihoods)))
if episode % visualize_interval == 0:
lineplot(metrics['step'][-len(metrics['step_reward']):], metrics['step_reward'], 'stepwise_reward', log_dir, xaxis='total step')
lineplot(metrics['episode'][-len(metrics['reward']):], metrics['reward'], 'reward', log_dir)
lineplot(metrics['collected_samples'][-len(metrics['reward']):], metrics['reward'], 'sample-reward', log_dir, xaxis='total step')
lineplot(metrics['episode'][-len(metrics['curiosity_reward']):], metrics['curiosity_reward'], 'curiosity_reward', log_dir)
lineplot(metrics['episode'][-len(metrics['likelihood']):], metrics['likelihood'], 'likelihood', log_dir)
# Agent update related metrics
if len(policy_losses) > 0 and not random_policy:
metrics['q1_loss'].append(np.mean(q1_losses))
metrics['policy_loss'].append(np.mean(policy_losses))
metrics['alpha_loss'].append(np.mean(alpha_losses))
metrics['alpha'].append(np.mean(alphas))
if episode % visualize_interval == 0:
lineplot(metrics['episode'][-len(metrics['q1_loss']):], metrics['q1_loss'], 'q1_loss', log_dir)
lineplot(metrics['episode'][-len(metrics['policy_loss']):], metrics['policy_loss'], 'policy_loss', log_dir)
lineplot(metrics['episode'][-len(metrics['alpha_loss']):], metrics['alpha_loss'], 'alpha_loss', log_dir)
lineplot(metrics['episode'][-len(metrics['alpha']):], metrics['alpha'], 'alpha', log_dir)
# Update VIME
if use_vime and len(memory) >= conf.random_sample_num:
for _ in range(conf.vime_update_per_episode):
batch_s, batch_a, _, batch_s_next, _ = memory.sample(conf.vime_update_batch_size)
elbo = vime.update_posterior(batch_s, batch_a, batch_s_next)
metrics['ELBO'].append(elbo)
lineplot(metrics['episode'][-len(metrics['ELBO']):], metrics['ELBO'], 'ELBO', log_dir)
if len(info_gains) > 0:
metrics['D_KL_median'].append(np.median(info_gains))
metrics['D_KL_mean'].append(np.mean(info_gains))
multiple_lineplot(metrics['episode'][-len(metrics['D_KL_median']):], np.array([metrics['D_KL_median'], metrics['D_KL_mean']]).T, 'D_KL', ['median', 'mean'], log_dir)
# Test current policy
if episode % conf.test_interval == 0:
rewards = []
for _ in range(conf.test_times):
o = env.reset()
done = False
episode_reward = 0
while not done:
a = agent.select_action(o, eval=True)
o_next, r, done, _ = env.step(a)
episode_reward += r
o = o_next
rewards.append(episode_reward)
mean, std = np.mean(rewards), np.std(rewards)
print("\nTEST AT EPISODE {} ({} episodes) --- Avg. Reward {:.2f} (+- {:.2f})".format(episode, conf.test_times, mean, std))
metrics['test_episode'].append(episode)
metrics['test_reward'].append(rewards)
lineplot(metrics['test_episode'][-len(metrics['test_reward']):], metrics['test_reward'], 'test_reward', log_dir)
# Save checkpoint
if episode % conf.checkpoint_interval == 0:
save_checkpoint()
save_checkpoint()
# Save the final model
torch.save({'agent': agent.state_dict()}, os.path.join(ckpt_dir, 'final_model.pth'))
def train(config_filepath, save_dir, device, visualize_interval):
conf = load_toml_config(config_filepath)
data_dir, log_dir = create_save_dir(save_dir)
# Save config file
shutil.copyfile(config_filepath,
os.path.join(save_dir, os.path.basename(config_filepath)))
device = torch.device(device)
# Set up log metrics
metrics = {
'episode': [],
'episodic_step': [],
'collected_total_samples': [],
'reward': [],
'q_loss': [],
'policy_loss': [],
'alpha_loss': [],
'alpha': [],
'policy_switch_epoch': [],
'policy_switch_sample': [],
'test_episode': [],
'test_reward': [],
}
policy_switch_samples = conf.policy_switch_samples if hasattr(
conf, "policy_switch_samples") else None
total_collected_samples = 0
# Create environment
env = make_env(conf.environment, render=False)
# Instantiate modules
memory = ReplayBuffer(int(conf.replay_buffer_capacity),
env.observation_space.shape, env.action_space.shape)
agent = getattr(agents, conf.agent_type)(env.observation_space,
env.action_space,
device=device,
**conf.agent)
# Load checkpoint if specified in config
if conf.checkpoint != '':
ckpt = torch.load(conf.checkpoint, map_location=device)
metrics = ckpt['metrics']
agent.load_state_dict(ckpt['agent'])
memory.load_state_dict(ckpt['memory'])
policy_switch_samples = ckpt['policy_switch_samples']
total_collected_samples = ckpt['total_collected_samples']
def save_checkpoint():
# Save checkpoint
ckpt = {
'metrics': metrics,
'agent': agent.state_dict(),
'memory': memory.state_dict(),
'policy_switch_samples': policy_switch_samples,
'total_collected_samples': total_collected_samples
}
path = os.path.join(data_dir, 'checkpoint.pth')
torch.save(ckpt, path)
# Save agent model only
model_ckpt = {'agent': agent.state_dict()}
model_path = os.path.join(data_dir, 'model.pth')
torch.save(model_ckpt, model_path)
# Save metrics only
metrics_ckpt = {'metrics': metrics}
metrics_path = os.path.join(data_dir, 'metrics.pth')
torch.save(metrics_ckpt, metrics_path)
# Train agent
init_episode = 0 if len(
metrics['episode']) == 0 else metrics['episode'][-1] + 1
pbar = tqdm.tqdm(range(init_episode, conf.episodes))
reward_moving_avg = None
agent_update_count = 0
for episode in pbar:
episodic_reward = 0
o = env.reset()
q1_loss, q2_loss, policy_loss, alpha_loss, alpha = None, None, None, None, None
for t in range(conf.horizon):
if total_collected_samples <= conf.random_sample_num: # Select random actions at the begining of training.
h = env.action_space.sample()
elif memory.step <= conf.random_sample_num: # Select actions from random latent variable soon after inserting a new subpolicy.
h = agent.select_action(o, random=True)
else:
h = agent.select_action(o)
a = agent.post_process_action(
o, h) # Convert abstract action h to actual action a
o_next, r, done, _ = env.step(a)
total_collected_samples += 1
episodic_reward += r
memory.push(o, h, r, o_next, done)
o = o_next
if memory.step > conf.random_sample_num:
# Update agent
batch_data = memory.sample(conf.agent_update_batch_size)
q1_loss, q2_loss, policy_loss, alpha_loss, alpha = agent.update_parameters(
batch_data, agent_update_count)
agent_update_count += 1
if done:
break
# Describe and save episodic metrics
reward_moving_avg = (
1. - MOVING_AVG_COEF
) * reward_moving_avg + MOVING_AVG_COEF * episodic_reward if reward_moving_avg else episodic_reward
pbar.set_description(
"EPISODE {} (total samples {}, subpolicy samples {}) --- Step {}, Reward {:.1f} (avg {:.1f})"
.format(episode, total_collected_samples, memory.step, t,
episodic_reward, reward_moving_avg))
metrics['episode'].append(episode)
metrics['reward'].append(episodic_reward)
metrics['episodic_step'].append(t)
metrics['collected_total_samples'].append(total_collected_samples)
if episode % visualize_interval == 0:
# Visualize metrics
lineplot(metrics['episode'][-len(metrics['reward']):],
metrics['reward'], 'REWARD', log_dir)
reward_avg = np.array(metrics['reward']) / np.array(
metrics['episodic_step'])
lineplot(metrics['episode'][-len(reward_avg):], reward_avg,
'AVG_REWARD', log_dir)
lineplot(
metrics['collected_total_samples'][-len(metrics['reward']):],
metrics['reward'],
'SAMPLE-REWARD',
log_dir,
xaxis='sample')
# Save metrics for agent update
if q1_loss is not None:
metrics['q_loss'].append(np.mean([q1_loss, q2_loss]))
metrics['policy_loss'].append(policy_loss)
metrics['alpha_loss'].append(alpha_loss)
metrics['alpha'].append(alpha)
if episode % visualize_interval == 0:
lineplot(metrics['episode'][-len(metrics['q_loss']):],
metrics['q_loss'], 'Q_LOSS', log_dir)
lineplot(metrics['episode'][-len(metrics['policy_loss']):],
metrics['policy_loss'], 'POLICY_LOSS', log_dir)
lineplot(metrics['episode'][-len(metrics['alpha_loss']):],
metrics['alpha_loss'], 'ALPHA_LOSS', log_dir)
lineplot(metrics['episode'][-len(metrics['alpha']):],
metrics['alpha'], 'ALPHA', log_dir)
# Insert new subpolicy layer and reset memory if a specific amount of samples is collected
if policy_switch_samples and len(
policy_switch_samples
) > 0 and total_collected_samples >= policy_switch_samples[0]:
print(
"----------------------\nInser new policy\n----------------------"
)
agent.insert_subpolicy()
memory.reset()
metrics['policy_switch_epoch'].append(episode)
metrics['policy_switch_sample'].append(total_collected_samples)
policy_switch_samples = policy_switch_samples[1:]
# Test a policy
if episode % conf.test_interval == 0:
test_rewards = []
for _ in range(conf.test_times):
episodic_reward = 0
obs = env.reset()
for t in range(conf.horizon):
h = agent.select_action(obs, eval=True)
a = agent.post_process_action(o, h)
obs_next, r, done, _ = env.step(a)
episodic_reward += r
obs = obs_next
if done:
break
test_rewards.append(episodic_reward)
test_reward_avg, test_reward_std = np.mean(test_rewards), np.std(
test_rewards)
print(" TEST --- ({} episodes) Reward {:.1f} (pm {:.1f})".format(
conf.test_times, test_reward_avg, test_reward_std))
metrics['test_episode'].append(episode)
metrics['test_reward'].append(test_rewards)
lineplot(metrics['test_episode'][-len(metrics['test_reward']):],
metrics['test_reward'], "TEST_REWARD", log_dir)
# Save checkpoint
if episode % conf.checkpoint_interval:
save_checkpoint()
# Save the final model
torch.save({'agent': agent.state_dict()},
os.path.join(data_dir, 'final_model.pth'))