def train_and_save_policy(env_name, seed, save_period, total_timesteps, hidden_dim=100):
save_dir = "trained_policy/%s/seed_%d_hidden_%d" % (env_name, seed, hidden_dim)
os.makedirs(save_dir, exist_ok=True)
if len(glob.glob("%s/step_*.pkl" % save_dir)) > 0:
print("already trained: %s" % save_dir)
return
def callback(_locals, _globals):
global n_steps
model_filepath = "%s/step_%d.pkl" % (save_dir, n_steps + 1)
if (n_steps + 1) % save_period == 0:
print('Saving a model to %s' % model_filepath)
model.save(model_filepath)
n_steps += 1
return True
global n_steps
n_steps = 0
env = gym.make(precise_env_name(env_name))
env.seed(seed)
set_global_seeds(seed)
model = SAC(env, ent_coef='auto', seed=seed, hidden_dim=hidden_dim)
model.learn(total_timesteps=total_timesteps, log_interval=10, seed=seed, callback=callback)
def initialize_policy(self):
if self.args.policy == 'dqn':
q_network = FlattenMlp(input_size=self.args.augmented_obs_dim,
output_size=self.args.act_space.n,
hidden_sizes=self.args.dqn_layers).to(
ptu.device)
self.agent = DQN(
q_network,
# optimiser_vae=self.optimizer_vae,
lr=self.args.policy_lr,
gamma=self.args.gamma,
tau=self.args.soft_target_tau,
).to(ptu.device)
else:
# assert self.args.act_space.__class__.__name__ == "Box", (
# "Can't train SAC with discrete action space!")
q1_network = FlattenMlp(
input_size=self.args.augmented_obs_dim + self.args.action_dim,
output_size=1,
hidden_sizes=self.args.dqn_layers).to(ptu.device)
q2_network = FlattenMlp(
input_size=self.args.augmented_obs_dim + self.args.action_dim,
output_size=1,
hidden_sizes=self.args.dqn_layers).to(ptu.device)
policy = TanhGaussianPolicy(
obs_dim=self.args.augmented_obs_dim,
action_dim=self.args.action_dim,
hidden_sizes=self.args.policy_layers).to(ptu.device)
self.agent = SAC(
policy,
q1_network,
q2_network,
actor_lr=self.args.actor_lr,
critic_lr=self.args.critic_lr,
gamma=self.args.gamma,
tau=self.args.soft_target_tau,
use_cql=self.args.use_cql if 'use_cql' in self.args else False,
alpha_cql=self.args.alpha_cql
if 'alpha_cql' in self.args else None,
entropy_alpha=self.args.entropy_alpha,
automatic_entropy_tuning=self.args.automatic_entropy_tuning,
alpha_lr=self.args.alpha_lr,
clip_grad_value=self.args.clip_grad_value,
).to(ptu.device)
def load_trained_agent(env_name, trained_policy_seed, trained_policy_step, bias_offset=0, seed=0, hidden_dim=64):
env = gym.make(precise_env_name(env_name))
trained_agent = SAC.load("trained_policy/%s/seed_%d_hidden_%d/step_%d.pkl" % (env_name, trained_policy_seed, hidden_dim, trained_policy_step), env, seed=seed, hidden_dim=hidden_dim)
parameters = trained_agent.get_parameters()
for i, parameter in enumerate(parameters):
name, value = parameter
if name == 'actor/f2_log_std/bias:0':
parameters[i] = (name, value + bias_offset)
trained_agent.load_parameters(parameters)
return trained_agent
def load_agent(args, agent_path):
q1_network = FlattenMlp(input_size=args.obs_dim + args.action_dim,
output_size=1,
hidden_sizes=args.dqn_layers)
q2_network = FlattenMlp(input_size=args.obs_dim + args.action_dim,
output_size=1,
hidden_sizes=args.dqn_layers)
policy = TanhGaussianPolicy(obs_dim=args.obs_dim,
action_dim=args.action_dim,
hidden_sizes=args.policy_layers)
agent = SAC(policy,
q1_network,
q2_network,
actor_lr=args.actor_lr,
critic_lr=args.critic_lr,
gamma=args.gamma,
tau=args.soft_target_tau,
entropy_alpha=args.entropy_alpha,
automatic_entropy_tuning=args.automatic_entropy_tuning,
alpha_lr=args.alpha_lr).to(ptu.device)
agent.load_state_dict(torch.load(agent_path))
return agent
class OfflineMetaLearner:
"""
Off-line Meta-Learner class, a.k.a no interaction with env.
"""
def __init__(self, args):
"""
Seeds everything.
Initialises: logger, environments, policy (+storage +optimiser).
"""
self.args = args
# make sure everything has the same seed
utl.seed(self.args.seed)
# initialize tensorboard logger
if self.args.log_tensorboard:
self.tb_logger = TBLogger(self.args)
self.args, env = off_utl.expand_args(self.args, include_act_space=True)
if self.args.act_space.__class__.__name__ == "Discrete":
self.args.policy = 'dqn'
else:
self.args.policy = 'sac'
# load buffers with data
if 'load_data' not in self.args or self.args.load_data:
goals, augmented_obs_dim = self.load_buffer(
env) # env is input just for possible relabelling option
self.args.augmented_obs_dim = augmented_obs_dim
self.goals = goals
# initialize policy
self.initialize_policy()
# load vae for inference in evaluation
self.load_vae()
# create environment for evaluation
self.env = make_env(
args.env_name,
args.max_rollouts_per_task,
presampled_tasks=args.presampled_tasks,
seed=args.seed,
)
# n_tasks=self.args.num_eval_tasks)
if self.args.env_name == 'GridNavi-v2':
self.env.unwrapped.goals = [
tuple(goal.astype(int)) for goal in self.goals
]
def initialize_policy(self):
if self.args.policy == 'dqn':
q_network = FlattenMlp(input_size=self.args.augmented_obs_dim,
output_size=self.args.act_space.n,
hidden_sizes=self.args.dqn_layers).to(
ptu.device)
self.agent = DQN(
q_network,
# optimiser_vae=self.optimizer_vae,
lr=self.args.policy_lr,
gamma=self.args.gamma,
tau=self.args.soft_target_tau,
).to(ptu.device)
else:
# assert self.args.act_space.__class__.__name__ == "Box", (
# "Can't train SAC with discrete action space!")
q1_network = FlattenMlp(
input_size=self.args.augmented_obs_dim + self.args.action_dim,
output_size=1,
hidden_sizes=self.args.dqn_layers).to(ptu.device)
q2_network = FlattenMlp(
input_size=self.args.augmented_obs_dim + self.args.action_dim,
output_size=1,
hidden_sizes=self.args.dqn_layers).to(ptu.device)
policy = TanhGaussianPolicy(
obs_dim=self.args.augmented_obs_dim,
action_dim=self.args.action_dim,
hidden_sizes=self.args.policy_layers).to(ptu.device)
self.agent = SAC(
policy,
q1_network,
q2_network,
actor_lr=self.args.actor_lr,
critic_lr=self.args.critic_lr,
gamma=self.args.gamma,
tau=self.args.soft_target_tau,
use_cql=self.args.use_cql if 'use_cql' in self.args else False,
alpha_cql=self.args.alpha_cql
if 'alpha_cql' in self.args else None,
entropy_alpha=self.args.entropy_alpha,
automatic_entropy_tuning=self.args.automatic_entropy_tuning,
alpha_lr=self.args.alpha_lr,
clip_grad_value=self.args.clip_grad_value,
).to(ptu.device)
def load_vae(self):
self.vae = VAE(self.args)
vae_models_path = os.path.join(self.args.vae_dir, self.args.env_name,
self.args.vae_model_name, 'models')
off_utl.load_trained_vae(self.vae, vae_models_path)
def load_buffer(self, env):
if self.args.hindsight_relabelling: # without arr_type loading -- GPU will explode
dataset, goals = off_utl.load_dataset(
data_dir=self.args.relabelled_data_dir,
args=self.args,
num_tasks=self.args.num_train_tasks,
allow_dense_data_loading=False,
arr_type='numpy')
dataset = off_utl.batch_to_trajectories(dataset, self.args)
dataset, goals = off_utl.mix_task_rollouts(
dataset, env, goals, self.args) # reward relabelling
dataset = off_utl.trajectories_to_batch(dataset)
else:
dataset, goals = off_utl.load_dataset(
data_dir=self.args.relabelled_data_dir,
args=self.args,
num_tasks=self.args.num_train_tasks,
allow_dense_data_loading=False,
arr_type='numpy')
augmented_obs_dim = dataset[0][0].shape[1]
self.storage = MultiTaskPolicyStorage(
max_replay_buffer_size=max([d[0].shape[0] for d in dataset]),
obs_dim=dataset[0][0].shape[1],
action_space=self.args.act_space,
tasks=range(len(goals)),
trajectory_len=self.args.trajectory_len)
for task, set in enumerate(dataset):
self.storage.add_samples(task,
observations=set[0],
actions=set[1],
rewards=set[2],
next_observations=set[3],
terminals=set[4])
return goals, augmented_obs_dim
def train(self):
self._start_training()
for iter_ in range(self.args.num_iters):
self.training_mode(True)
indices = np.random.choice(len(self.goals), self.args.meta_batch)
train_stats = self.update(indices)
self.training_mode(False)
self.log(iter_ + 1, train_stats)
def update(self, tasks):
rl_losses_agg = {}
for update in range(self.args.rl_updates_per_iter):
# sample random RL batch
obs, actions, rewards, next_obs, terms = self.sample_rl_batch(
tasks, self.args.batch_size)
# flatten out task dimension
t, b, _ = obs.size()
obs = obs.view(t * b, -1)
actions = actions.view(t * b, -1)
rewards = rewards.view(t * b, -1)
next_obs = next_obs.view(t * b, -1)
terms = terms.view(t * b, -1)
# RL update
rl_losses = self.agent.update(obs,
actions,
rewards,
next_obs,
terms,
action_space=self.env.action_space)
for k, v in rl_losses.items():
if update == 0: # first iterate - create list
rl_losses_agg[k] = [v]
else: # append values
rl_losses_agg[k].append(v)
# take mean
for k in rl_losses_agg:
rl_losses_agg[k] = np.mean(rl_losses_agg[k])
self._n_rl_update_steps_total += self.args.rl_updates_per_iter
return rl_losses_agg
def evaluate(self):
num_episodes = self.args.max_rollouts_per_task
num_steps_per_episode = self.env.unwrapped._max_episode_steps
num_tasks = self.args.num_eval_tasks
obs_size = self.env.unwrapped.observation_space.shape[0]
returns_per_episode = np.zeros((num_tasks, num_episodes))
success_rate = np.zeros(num_tasks)
rewards = np.zeros((num_tasks, self.args.trajectory_len))
reward_preds = np.zeros((num_tasks, self.args.trajectory_len))
observations = np.zeros(
(num_tasks, self.args.trajectory_len + 1, obs_size))
if self.args.policy == 'sac':
log_probs = np.zeros((num_tasks, self.args.trajectory_len))
# This part is very specific for the Semi-Circle env
# if self.args.env_name == 'PointRobotSparse-v0':
# reward_belief = np.zeros((num_tasks, self.args.trajectory_len))
#
# low_x, high_x, low_y, high_y = -2., 2., -1., 2.
# resolution = 0.1
# grid_x = np.arange(low_x, high_x + resolution, resolution)
# grid_y = np.arange(low_y, high_y + resolution, resolution)
# centers_x = (grid_x[:-1] + grid_x[1:]) / 2
# centers_y = (grid_y[:-1] + grid_y[1:]) / 2
# yv, xv = np.meshgrid(centers_y, centers_x, sparse=False, indexing='ij')
# centers = np.vstack([xv.ravel(), yv.ravel()]).T
# n_grid_points = centers.shape[0]
# reward_belief_discretized = np.zeros((num_tasks, self.args.trajectory_len, centers.shape[0]))
for task_loop_i, task in enumerate(
self.env.unwrapped.get_all_eval_task_idx()):
obs = ptu.from_numpy(self.env.reset(task))
obs = obs.reshape(-1, obs.shape[-1])
step = 0
# get prior parameters
with torch.no_grad():
task_sample, task_mean, task_logvar, hidden_state = self.vae.encoder.prior(
batch_size=1)
observations[task_loop_i,
step, :] = ptu.get_numpy(obs[0, :obs_size])
for episode_idx in range(num_episodes):
running_reward = 0.
for step_idx in range(num_steps_per_episode):
# add distribution parameters to observation - policy is conditioned on posterior
augmented_obs = self.get_augmented_obs(
obs, task_mean, task_logvar)
if self.args.policy == 'dqn':
action, value = self.agent.act(obs=augmented_obs,
deterministic=True)
else:
action, _, _, log_prob = self.agent.act(
obs=augmented_obs,
deterministic=self.args.eval_deterministic,
return_log_prob=True)
# observe reward and next obs
next_obs, reward, done, info = utl.env_step(
self.env, action.squeeze(dim=0))
running_reward += reward.item()
# done_rollout = False if ptu.get_numpy(done[0][0]) == 0. else True
# update encoding
task_sample, task_mean, task_logvar, hidden_state = self.update_encoding(
obs=next_obs,
action=action,
reward=reward,
done=done,
hidden_state=hidden_state)
rewards[task_loop_i, step] = reward.item()
reward_preds[task_loop_i, step] = ptu.get_numpy(
self.vae.reward_decoder(task_sample, next_obs, obs,
action)[0, 0])
# This part is very specific for the Semi-Circle env
# if self.args.env_name == 'PointRobotSparse-v0':
# reward_belief[task, step] = ptu.get_numpy(
# self.vae.compute_belief_reward(task_mean, task_logvar, obs, next_obs, action)[0])
#
# reward_belief_discretized[task, step, :] = ptu.get_numpy(
# self.vae.compute_belief_reward(task_mean.repeat(n_grid_points, 1),
# task_logvar.repeat(n_grid_points, 1),
# None,
# torch.cat((ptu.FloatTensor(centers),
# ptu.zeros(centers.shape[0], 1)), dim=-1).unsqueeze(0),
# None)[:, 0])
observations[task_loop_i, step + 1, :] = ptu.get_numpy(
next_obs[0, :obs_size])
if self.args.policy != 'dqn':
log_probs[task_loop_i,
step] = ptu.get_numpy(log_prob[0])
if "is_goal_state" in dir(
self.env.unwrapped
) and self.env.unwrapped.is_goal_state():
success_rate[task_loop_i] = 1.
# set: obs <- next_obs
obs = next_obs.clone()
step += 1
returns_per_episode[task_loop_i, episode_idx] = running_reward
if self.args.policy == 'dqn':
return returns_per_episode, success_rate, observations, rewards, reward_preds
# This part is very specific for the Semi-Circle env
# elif self.args.env_name == 'PointRobotSparse-v0':
# return returns_per_episode, success_rate, log_probs, observations, \
# rewards, reward_preds, reward_belief, reward_belief_discretized, centers
else:
return returns_per_episode, success_rate, log_probs, observations, rewards, reward_preds
def log(self, iteration, train_stats):
# --- save model ---
if iteration % self.args.save_interval == 0:
save_path = os.path.join(self.tb_logger.full_output_folder,
'models')
if not os.path.exists(save_path):
os.mkdir(save_path)
torch.save(
self.agent.state_dict(),
os.path.join(save_path, "agent{0}.pt".format(iteration)))
if iteration % self.args.log_interval == 0:
if self.args.policy == 'dqn':
returns, success_rate, observations, rewards, reward_preds = self.evaluate(
)
# This part is super specific for the Semi-Circle env
# elif self.args.env_name == 'PointRobotSparse-v0':
# returns, success_rate, log_probs, observations, \
# rewards, reward_preds, reward_belief, reward_belief_discretized, points = self.evaluate()
else:
returns, success_rate, log_probs, observations, rewards, reward_preds = self.evaluate(
)
if self.args.log_tensorboard:
tasks_to_vis = np.random.choice(self.args.num_eval_tasks, 5)
for i, task in enumerate(tasks_to_vis):
self.env.reset(task)
if PLOT_VIS:
self.tb_logger.writer.add_figure(
'policy_vis/task_{}'.format(i),
utl_eval.plot_rollouts(observations[task, :],
self.env),
self._n_rl_update_steps_total)
self.tb_logger.writer.add_figure(
'reward_prediction_train/task_{}'.format(i),
utl_eval.plot_rew_pred_vs_rew(rewards[task, :],
reward_preds[task, :]),
self._n_rl_update_steps_total)
# self.tb_logger.writer.add_figure('reward_prediction_train/task_{}'.format(i),
# utl_eval.plot_rew_pred_vs_reward_belief_vs_rew(rewards[task, :],
# reward_preds[task, :],
# reward_belief[task, :]),
# self._n_rl_update_steps_total)
# if self.args.env_name == 'PointRobotSparse-v0': # This part is super specific for the Semi-Circle env
# for t in range(0, int(self.args.trajectory_len/4), 3):
# self.tb_logger.writer.add_figure('discrete_belief_reward_pred_task_{}/timestep_{}'.format(i, t),
# utl_eval.plot_discretized_belief_halfcircle(reward_belief_discretized[task, t, :],
# points, self.env,
# observations[task, :t+1]),
# self._n_rl_update_steps_total)
if self.args.max_rollouts_per_task > 1:
for episode_idx in range(self.args.max_rollouts_per_task):
self.tb_logger.writer.add_scalar(
'returns_multi_episode/episode_{}'.format(
episode_idx + 1),
np.mean(returns[:, episode_idx]),
self._n_rl_update_steps_total)
self.tb_logger.writer.add_scalar(
'returns_multi_episode/sum',
np.mean(np.sum(returns, axis=-1)),
self._n_rl_update_steps_total)
self.tb_logger.writer.add_scalar(
'returns_multi_episode/success_rate',
np.mean(success_rate), self._n_rl_update_steps_total)
else:
self.tb_logger.writer.add_scalar(
'returns/returns_mean', np.mean(returns),
self._n_rl_update_steps_total)
self.tb_logger.writer.add_scalar(
'returns/returns_std', np.std(returns),
self._n_rl_update_steps_total)
self.tb_logger.writer.add_scalar(
'returns/success_rate', np.mean(success_rate),
self._n_rl_update_steps_total)
if self.args.policy == 'dqn':
self.tb_logger.writer.add_scalar(
'rl_losses/qf_loss_vs_n_updates',
train_stats['qf_loss'], self._n_rl_update_steps_total)
self.tb_logger.writer.add_scalar(
'weights/q_network',
list(self.agent.qf.parameters())[0].mean(),
self._n_rl_update_steps_total)
if list(self.agent.qf.parameters())[0].grad is not None:
param_list = list(self.agent.qf.parameters())
self.tb_logger.writer.add_scalar(
'gradients/q_network',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_rl_update_steps_total)
self.tb_logger.writer.add_scalar(
'weights/q_target',
list(self.agent.target_qf.parameters())[0].mean(),
self._n_rl_update_steps_total)
if list(self.agent.target_qf.parameters()
)[0].grad is not None:
param_list = list(self.agent.target_qf.parameters())
self.tb_logger.writer.add_scalar(
'gradients/q_target',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_rl_update_steps_total)
else:
self.tb_logger.writer.add_scalar(
'policy/log_prob', np.mean(log_probs),
self._n_rl_update_steps_total)
self.tb_logger.writer.add_scalar(
'rl_losses/qf1_loss', train_stats['qf1_loss'],
self._n_rl_update_steps_total)
self.tb_logger.writer.add_scalar(
'rl_losses/qf2_loss', train_stats['qf2_loss'],
self._n_rl_update_steps_total)
self.tb_logger.writer.add_scalar(
'rl_losses/policy_loss', train_stats['policy_loss'],
self._n_rl_update_steps_total)
self.tb_logger.writer.add_scalar(
'rl_losses/alpha_entropy_loss',
train_stats['alpha_entropy_loss'],
self._n_rl_update_steps_total)
# weights and gradients
self.tb_logger.writer.add_scalar(
'weights/q1_network',
list(self.agent.qf1.parameters())[0].mean(),
self._n_rl_update_steps_total)
if list(self.agent.qf1.parameters())[0].grad is not None:
param_list = list(self.agent.qf1.parameters())
self.tb_logger.writer.add_scalar(
'gradients/q1_network',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_rl_update_steps_total)
self.tb_logger.writer.add_scalar(
'weights/q1_target',
list(self.agent.qf1_target.parameters())[0].mean(),
self._n_rl_update_steps_total)
if list(self.agent.qf1_target.parameters()
)[0].grad is not None:
param_list = list(self.agent.qf1_target.parameters())
self.tb_logger.writer.add_scalar(
'gradients/q1_target',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_rl_update_steps_total)
self.tb_logger.writer.add_scalar(
'weights/q2_network',
list(self.agent.qf2.parameters())[0].mean(),
self._n_rl_update_steps_total)
if list(self.agent.qf2.parameters())[0].grad is not None:
param_list = list(self.agent.qf2.parameters())
self.tb_logger.writer.add_scalar(
'gradients/q2_network',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_rl_update_steps_total)
self.tb_logger.writer.add_scalar(
'weights/q2_target',
list(self.agent.qf2_target.parameters())[0].mean(),
self._n_rl_update_steps_total)
if list(self.agent.qf2_target.parameters()
)[0].grad is not None:
param_list = list(self.agent.qf2_target.parameters())
self.tb_logger.writer.add_scalar(
'gradients/q2_target',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_rl_update_steps_total)
self.tb_logger.writer.add_scalar(
'weights/policy',
list(self.agent.policy.parameters())[0].mean(),
self._n_rl_update_steps_total)
if list(self.agent.policy.parameters()
)[0].grad is not None:
param_list = list(self.agent.policy.parameters())
self.tb_logger.writer.add_scalar(
'gradients/policy',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_rl_update_steps_total)
for k, v in [
('num_rl_updates', self._n_rl_update_steps_total),
('time_elapsed', time.time() - self._start_time),
('iteration', iteration),
]:
self.tb_logger.writer.add_scalar(k, v,
self._n_rl_update_steps_total)
self.tb_logger.finish_iteration(iteration)
print(
"Iteration -- {}, Success rate -- {:.3f}, Avg. return -- {:.3f}, Elapsed time {:5d}[s]"
.format(iteration, np.mean(success_rate),
np.mean(np.sum(returns, axis=-1)),
int(time.time() - self._start_time)))
def sample_rl_batch(self, tasks, batch_size):
''' sample batch of unordered rl training data from a list/array of tasks '''
# this batch consists of transitions sampled randomly from replay buffer
batches = [
ptu.np_to_pytorch_batch(self.storage.random_batch(
task, batch_size)) for task in tasks
]
unpacked = [utl.unpack_batch(batch) for batch in batches]
# group elements together
unpacked = [[x[i] for x in unpacked] for i in range(len(unpacked[0]))]
unpacked = [torch.cat(x, dim=0) for x in unpacked]
return unpacked
def _start_training(self):
self._n_rl_update_steps_total = 0
self._start_time = time.time()
def training_mode(self, mode):
self.agent.train(mode)
def update_encoding(self, obs, action, reward, done, hidden_state):
# reset hidden state of the recurrent net when the task is done
hidden_state = self.vae.encoder.reset_hidden(hidden_state, done)
with torch.no_grad(): # size should be (batch, dim)
task_sample, task_mean, task_logvar, hidden_state = self.vae.encoder(
actions=action.float(),
states=obs,
rewards=reward,
hidden_state=hidden_state,
return_prior=False)
return task_sample, task_mean, task_logvar, hidden_state
@staticmethod
def get_augmented_obs(obs, mean, logvar):
mean = mean.reshape((-1, mean.shape[-1]))
logvar = logvar.reshape((-1, logvar.shape[-1]))
return torch.cat((obs, mean, logvar), dim=-1)
def load_model(self, agent_path, device='cpu'):
self.agent.load_state_dict(torch.load(agent_path, map_location=device))
self.load_vae()
self.training_mode(False)
def run(config):
torch.set_num_threads(1)
env_descr = 'map%i_%iagents_task%i' % (config.map_ind, config.num_agents,
config.task_config)
model_dir = Path('./models') / config.env_type / env_descr / config.model_name
if not model_dir.exists():
run_num = 1
else:
exst_run_nums = [int(str(folder.name).split('run')[1]) for folder in
model_dir.iterdir() if
str(folder.name).startswith('run')]
if len(exst_run_nums) == 0:
run_num = 1
else:
run_num = max(exst_run_nums) + 1
curr_run = 'run%i' % run_num
run_dir = model_dir / curr_run
log_dir = run_dir / 'logs'
os.makedirs(log_dir)
logger = SummaryWriter(str(log_dir))
torch.manual_seed(run_num)
np.random.seed(run_num)
env = make_parallel_env(config, run_num)
if config.nonlinearity == 'relu':
nonlin = torch.nn.functional.relu
elif config.nonlinearity == 'leaky_relu':
nonlin = torch.nn.functional.leaky_relu
if config.intrinsic_reward == 0:
n_intr_rew_types = 0
sep_extr_head = True
else:
n_intr_rew_types = len(config.explr_types)
sep_extr_head = False
n_rew_heads = n_intr_rew_types + int(sep_extr_head)
model = SAC.init_from_env(env,
nagents=config.num_agents,
tau=config.tau,
hard_update_interval=config.hard_update,
pi_lr=config.pi_lr,
q_lr=config.q_lr,
phi_lr=config.phi_lr,
adam_eps=config.adam_eps,
q_decay=config.q_decay,
phi_decay=config.phi_decay,
gamma_e=config.gamma_e,
gamma_i=config.gamma_i,
pol_hidden_dim=config.pol_hidden_dim,
critic_hidden_dim=config.critic_hidden_dim,
nonlin=nonlin,
reward_scale=config.reward_scale,
head_reward_scale=config.head_reward_scale,
beta=config.beta,
n_intr_rew_types=n_intr_rew_types,
sep_extr_head=sep_extr_head)
replay_buffer = ReplayBuffer(config.buffer_length, model.nagents,
env.state_space,
env.observation_space,
env.action_space)
intr_rew_rms = [[RunningMeanStd()
for i in range(config.num_agents)]
for j in range(n_intr_rew_types)]
eps_this_turn = 0 # episodes so far this turn
active_envs = np.ones(config.n_rollout_threads) # binary indicator of whether env is active
env_times = np.zeros(config.n_rollout_threads, dtype=int)
env_ep_extr_rews = np.zeros(config.n_rollout_threads)
env_extr_rets = np.zeros(config.n_rollout_threads)
env_ep_intr_rews = [[np.zeros(config.n_rollout_threads) for i in range(config.num_agents)]
for j in range(n_intr_rew_types)]
recent_ep_extr_rews = deque(maxlen=100)
recent_ep_intr_rews = [[deque(maxlen=100) for i in range(config.num_agents)]
for j in range(n_intr_rew_types)]
recent_ep_lens = deque(maxlen=100)
recent_found_treasures = [deque(maxlen=100) for i in range(config.num_agents)]
meta_turn_rets = []
extr_ret_rms = [RunningMeanStd() for i in range(n_rew_heads)]
t = 0
steps_since_update = 0
state, obs = env.reset()
while t < config.train_time:
model.prep_rollouts(device='cuda' if config.gpu_rollout else 'cpu')
# convert to torch tensor
torch_obs = apply_to_all_elements(obs, lambda x: torch.tensor(x, dtype=torch.float32, device='cuda' if config.gpu_rollout else 'cpu'))
# get actions as torch tensors
torch_agent_actions = model.step(torch_obs, explore=True)
# convert actions to numpy arrays
agent_actions = apply_to_all_elements(torch_agent_actions, lambda x: x.cpu().data.numpy())
# rearrange actions to be per environment
actions = [[ac[i] for ac in agent_actions] for i in range(int(active_envs.sum()))]
try:
with timeout(seconds=1):
next_state, next_obs, rewards, dones, infos = env.step(actions, env_mask=active_envs)
# either environment got stuck or vizdoom crashed (vizdoom is unstable w/ multi-agent scenarios)
except (TimeoutError, ViZDoomErrorException, ViZDoomIsNotRunningException, ViZDoomUnexpectedExitException) as e:
print("Environments are broken...")
env.close(force=True)
print("Closed environments, starting new...")
env = make_parallel_env(config, run_num)
state, obs = env.reset()
env_ep_extr_rews[active_envs.astype(bool)] = 0.0
env_extr_rets[active_envs.astype(bool)] = 0.0
for i in range(n_intr_rew_types):
for j in range(config.num_agents):
env_ep_intr_rews[i][j][active_envs.astype(bool)] = 0.0
env_times = np.zeros(config.n_rollout_threads, dtype=int)
state = apply_to_all_elements(state, lambda x: x[active_envs.astype(bool)])
obs = apply_to_all_elements(obs, lambda x: x[active_envs.astype(bool)])
continue
steps_since_update += int(active_envs.sum())
if config.intrinsic_reward == 1:
# if using state-visit counts, store state indices
# shape = (n_envs, n_agents, n_inds)
state_inds = np.array([i['visit_count_lookup'] for i in infos],
dtype=int)
state_inds_t = state_inds.transpose(1, 0, 2)
novelties = get_count_based_novelties(env, state_inds_t, device='cpu')
intr_rews = get_intrinsic_rewards(novelties, config, intr_rew_rms,
update_irrms=True, active_envs=active_envs,
device='cpu')
intr_rews = apply_to_all_elements(intr_rews, lambda x: x.numpy().flatten())
else:
intr_rews = None
state_inds = None
state_inds_t = None
replay_buffer.push(state, obs, agent_actions, rewards, next_state, next_obs, dones,
state_inds=state_inds)
env_ep_extr_rews[active_envs.astype(bool)] += np.array(rewards)
env_extr_rets[active_envs.astype(bool)] += np.array(rewards) * config.gamma_e**(env_times[active_envs.astype(bool)])
env_times += active_envs.astype(int)
if intr_rews is not None:
for i in range(n_intr_rew_types):
for j in range(config.num_agents):
env_ep_intr_rews[i][j][active_envs.astype(bool)] += intr_rews[i][j]
over_time = env_times >= config.max_episode_length
full_dones = np.zeros(config.n_rollout_threads)
for i, env_i in enumerate(np.where(active_envs)[0]):
full_dones[env_i] = dones[i]
need_reset = np.logical_or(full_dones, over_time)
# create masks ONLY for active envs
active_over_time = env_times[active_envs.astype(bool)] >= config.max_episode_length
active_need_reset = np.logical_or(dones, active_over_time)
if any(need_reset):
try:
with timeout(seconds=1):
# reset any environments that are past the max number of time steps or done
state, obs = env.reset(need_reset=need_reset)
# either environment got stuck or vizdoom crashed (vizdoom is unstable w/ multi-agent scenarios)
except (TimeoutError, ViZDoomErrorException, ViZDoomIsNotRunningException, ViZDoomUnexpectedExitException) as e:
print("Environments are broken...")
env.close(force=True)
print("Closed environments, starting new...")
env = make_parallel_env(config, run_num)
state, obs = env.reset()
# other envs that were force reset (rest taken care of in subsequent code)
other_reset = np.logical_not(need_reset)
env_ep_extr_rews[other_reset.astype(bool)] = 0.0
env_extr_rets[other_reset.astype(bool)] = 0.0
for i in range(n_intr_rew_types):
for j in range(config.num_agents):
env_ep_intr_rews[i][j][other_reset.astype(bool)] = 0.0
env_times = np.zeros(config.n_rollout_threads, dtype=int)
else:
state, obs = next_state, next_obs
for env_i in np.where(need_reset)[0]:
recent_ep_extr_rews.append(env_ep_extr_rews[env_i])
meta_turn_rets.append(env_extr_rets[env_i])
if intr_rews is not None:
for j in range(n_intr_rew_types):
for k in range(config.num_agents):
# record intrinsic rewards per step (so we don't confuse shorter episodes with less intrinsic rewards)
recent_ep_intr_rews[j][k].append(env_ep_intr_rews[j][k][env_i] / env_times[env_i])
env_ep_intr_rews[j][k][env_i] = 0
recent_ep_lens.append(env_times[env_i])
env_times[env_i] = 0
env_ep_extr_rews[env_i] = 0
env_extr_rets[env_i] = 0
eps_this_turn += 1
if eps_this_turn + active_envs.sum() - 1 >= config.metapol_episodes:
active_envs[env_i] = 0
for i in np.where(active_need_reset)[0]:
for j in range(config.num_agents):
# len(infos) = number of active envs
recent_found_treasures[j].append(infos[i]['n_found_treasures'][j])
if eps_this_turn >= config.metapol_episodes:
if not config.uniform_heads and n_rew_heads > 1:
meta_turn_rets = np.array(meta_turn_rets)
if all(errms.count < 1 for errms in extr_ret_rms):
for errms in extr_ret_rms:
errms.mean = meta_turn_rets.mean()
extr_ret_rms[model.curr_pol_heads[0]].update(meta_turn_rets)
for i in range(config.metapol_updates):
model.update_heads_onpol(meta_turn_rets, extr_ret_rms, logger=logger)
pol_heads = model.sample_pol_heads(uniform=config.uniform_heads)
model.set_pol_heads(pol_heads)
eps_this_turn = 0
meta_turn_rets = []
active_envs = np.ones(config.n_rollout_threads)
if any(need_reset): # reset returns state and obs for all envs, so make sure we're only looking at active
state = apply_to_all_elements(state, lambda x: x[active_envs.astype(bool)])
obs = apply_to_all_elements(obs, lambda x: x[active_envs.astype(bool)])
if (len(replay_buffer) >= max(config.batch_size,
config.steps_before_update) and
(steps_since_update >= config.steps_per_update)):
steps_since_update = 0
print('Updating at time step %i' % t)
model.prep_training(device='cuda' if config.use_gpu else 'cpu')
for u_i in range(config.num_updates):
sample = replay_buffer.sample(config.batch_size,
to_gpu=config.use_gpu,
state_inds=(config.intrinsic_reward == 1))
if config.intrinsic_reward == 0: # no intrinsic reward
intr_rews = None
state_inds = None
else:
sample, state_inds = sample
novelties = get_count_based_novelties(
env, state_inds,
device='cuda' if config.use_gpu else 'cpu')
intr_rews = get_intrinsic_rewards(novelties, config, intr_rew_rms,
update_irrms=False,
device='cuda' if config.use_gpu else 'cpu')
model.update_critic(sample, logger=logger, intr_rews=intr_rews)
model.update_policies(sample, logger=logger)
model.update_all_targets()
if len(recent_ep_extr_rews) > 10:
logger.add_scalar('episode_rewards/extrinsic/mean',
np.mean(recent_ep_extr_rews), t)
logger.add_scalar('episode_lengths/mean',
np.mean(recent_ep_lens), t)
if config.intrinsic_reward == 1:
for i in range(n_intr_rew_types):
for j in range(config.num_agents):
logger.add_scalar('episode_rewards/intrinsic%i_agent%i/mean' % (i, j),
np.mean(recent_ep_intr_rews[i][j]), t)
for i in range(config.num_agents):
logger.add_scalar('agent%i/n_found_treasures' % i, np.mean(recent_found_treasures[i]), t)
logger.add_scalar('total_n_found_treasures', sum(np.array(recent_found_treasures[i]) for i in range(config.num_agents)).mean(), t)
if t % config.save_interval < config.n_rollout_threads:
model.prep_training(device='cpu')
os.makedirs(run_dir / 'incremental', exist_ok=True)
model.save(run_dir / 'incremental' / ('model_%isteps.pt' % (t + 1)))
model.save(run_dir / 'model.pt')
t += active_envs.sum()
model.prep_training(device='cpu')
model.save(run_dir / 'model.pt')
logger.close()
env.close(force=(config.env_type == 'vizdoom'))
class Learner:
"""
Learner class.
"""
def __init__(self, args):
"""
Seeds everything.
Initialises: logger, environments, policy (+storage +optimiser).
"""
self.args = args
# make sure everything has the same seed
utl.seed(self.args.seed)
# initialise environment
self.env = make_env(
self.args.env_name,
self.args.max_rollouts_per_task,
seed=self.args.seed,
n_tasks=1,
modify_init_state_dist=self.args.modify_init_state_dist
if 'modify_init_state_dist' in self.args else False,
on_circle_init_state=self.args.on_circle_init_state
if 'on_circle_init_state' in self.args else True)
# saving buffer with task in name folder
if hasattr(self.args, 'save_buffer') and self.args.save_buffer:
env_dir = os.path.join(self.args.main_save_dir,
'{}'.format(self.args.env_name))
goal = self.env.unwrapped._goal
self.output_dir = os.path.join(
env_dir, self.args.save_dir,
'seed_{}_'.format(self.args.seed) +
off_utl.create_goal_path_ext_from_goal(goal))
if self.args.save_models or self.args.save_buffer:
os.makedirs(self.output_dir, exist_ok=True)
config_utl.save_config_file(args, self.output_dir)
# initialize tensorboard logger
if self.args.log_tensorboard:
self.tb_logger = TBLogger(self.args)
# if not self.args.log_tensorboard:
# self.save_config_json_file()
# unwrapped env to get some info about the environment
unwrapped_env = self.env.unwrapped
# calculate what the maximum length of the trajectories is
args.max_trajectory_len = unwrapped_env._max_episode_steps
args.max_trajectory_len *= self.args.max_rollouts_per_task
self.args.max_trajectory_len = args.max_trajectory_len
# get action / observation dimensions
if isinstance(self.env.action_space, gym.spaces.discrete.Discrete):
self.args.action_dim = 1
else:
self.args.action_dim = self.env.action_space.shape[0]
self.args.obs_dim = self.env.observation_space.shape[0]
self.args.num_states = unwrapped_env.num_states if hasattr(
unwrapped_env, 'num_states') else None
self.args.act_space = self.env.action_space
# simulate env step to get reward types
_, _, _, info = unwrapped_env.step(unwrapped_env.action_space.sample())
reward_types = [
reward_type for reward_type in list(info.keys())
if reward_type.startswith('reward')
]
# support dense rewards training (if exists)
self.args.dense_train_sparse_test = self.args.dense_train_sparse_test \
if 'dense_train_sparse_test' in self.args else False
# initialize policy
self.initialize_policy()
# initialize buffer for RL updates
self.policy_storage = MultiTaskPolicyStorage(
max_replay_buffer_size=int(self.args.policy_buffer_size),
obs_dim=self.args.obs_dim,
action_space=self.env.action_space,
tasks=[0],
trajectory_len=args.max_trajectory_len,
num_reward_arrays=len(reward_types)
if reward_types and self.args.dense_train_sparse_test else 1,
reward_types=reward_types,
)
self.args.belief_reward = False # initialize arg to not use belief rewards
def initialize_policy(self):
if self.args.policy == 'dqn':
assert self.args.act_space.__class__.__name__ == "Discrete", (
"Can't train DQN with continuous action space!")
q_network = FlattenMlp(input_size=self.args.obs_dim,
output_size=self.args.act_space.n,
hidden_sizes=self.args.dqn_layers)
self.agent = DQN(
q_network,
# optimiser_vae=self.optimizer_vae,
lr=self.args.policy_lr,
gamma=self.args.gamma,
eps_init=self.args.dqn_epsilon_init,
eps_final=self.args.dqn_epsilon_final,
exploration_iters=self.args.dqn_exploration_iters,
tau=self.args.soft_target_tau,
).to(ptu.device)
# elif self.args.policy == 'ddqn':
# assert self.args.act_space.__class__.__name__ == "Discrete", (
# "Can't train DDQN with continuous action space!")
# q_network = FlattenMlp(input_size=self.args.obs_dim,
# output_size=self.args.act_space.n,
# hidden_sizes=self.args.dqn_layers)
# self.agent = DoubleDQN(
# q_network,
# # optimiser_vae=self.optimizer_vae,
# lr=self.args.policy_lr,
# eps_optim=self.args.dqn_eps,
# alpha_optim=self.args.dqn_alpha,
# gamma=self.args.gamma,
# eps_init=self.args.dqn_epsilon_init,
# eps_final=self.args.dqn_epsilon_final,
# exploration_iters=self.args.dqn_exploration_iters,
# tau=self.args.soft_target_tau,
# ).to(ptu.device)
elif self.args.policy == 'sac':
assert self.args.act_space.__class__.__name__ == "Box", (
"Can't train SAC with discrete action space!")
q1_network = FlattenMlp(input_size=self.args.obs_dim +
self.args.action_dim,
output_size=1,
hidden_sizes=self.args.dqn_layers)
q2_network = FlattenMlp(input_size=self.args.obs_dim +
self.args.action_dim,
output_size=1,
hidden_sizes=self.args.dqn_layers)
policy = TanhGaussianPolicy(obs_dim=self.args.obs_dim,
action_dim=self.args.action_dim,
hidden_sizes=self.args.policy_layers)
self.agent = SAC(
policy,
q1_network,
q2_network,
actor_lr=self.args.actor_lr,
critic_lr=self.args.critic_lr,
gamma=self.args.gamma,
tau=self.args.soft_target_tau,
entropy_alpha=self.args.entropy_alpha,
automatic_entropy_tuning=self.args.automatic_entropy_tuning,
alpha_lr=self.args.alpha_lr).to(ptu.device)
else:
raise NotImplementedError
def train(self):
"""
meta-training loop
"""
self._start_training()
self.task_idx = 0
for iter_ in range(self.args.num_iters):
self.training_mode(True)
if iter_ == 0:
print('Collecting initial pool of data..')
self.env.reset_task(idx=self.task_idx)
self.collect_rollouts(
num_rollouts=self.args.num_init_rollouts_pool,
random_actions=True)
print('Done!')
# collect data from subset of train tasks
self.env.reset_task(idx=self.task_idx)
self.collect_rollouts(num_rollouts=self.args.num_rollouts_per_iter)
# update
train_stats = self.update([self.task_idx])
self.training_mode(False)
if self.args.policy == 'dqn':
self.agent.set_exploration_parameter(iter_ + 1)
# evaluate and log
if (iter_ + 1) % self.args.log_interval == 0:
self.log(iter_ + 1, train_stats)
def update(self, tasks):
'''
RL updates
:param tasks: list/array of task indices. perform update based on the tasks
:return:
'''
# --- RL TRAINING ---
rl_losses_agg = {}
for update in range(self.args.rl_updates_per_iter):
# sample random RL batch
obs, actions, rewards, next_obs, terms = self.sample_rl_batch(
tasks, self.args.batch_size)
# flatten out task dimension
t, b, _ = obs.size()
obs = obs.view(t * b, -1)
actions = actions.view(t * b, -1)
rewards = rewards.view(t * b, -1)
next_obs = next_obs.view(t * b, -1)
terms = terms.view(t * b, -1)
# RL update
rl_losses = self.agent.update(obs, actions, rewards, next_obs,
terms)
for k, v in rl_losses.items():
if update == 0: # first iterate - create list
rl_losses_agg[k] = [v]
else: # append values
rl_losses_agg[k].append(v)
# take mean
for k in rl_losses_agg:
rl_losses_agg[k] = np.mean(rl_losses_agg[k])
self._n_rl_update_steps_total += self.args.rl_updates_per_iter
return rl_losses_agg
def evaluate(self, tasks):
num_episodes = self.args.max_rollouts_per_task
num_steps_per_episode = self.env.unwrapped._max_episode_steps
returns_per_episode = np.zeros((len(tasks), num_episodes))
success_rate = np.zeros(len(tasks))
if self.args.policy == 'dqn':
values = np.zeros((len(tasks), self.args.max_trajectory_len))
else:
obs_size = self.env.unwrapped.observation_space.shape[0]
observations = np.zeros(
(len(tasks), self.args.max_trajectory_len + 1, obs_size))
log_probs = np.zeros((len(tasks), self.args.max_trajectory_len))
for task_idx, task in enumerate(tasks):
obs = ptu.from_numpy(self.env.reset(task))
obs = obs.reshape(-1, obs.shape[-1])
step = 0
if self.args.policy == 'sac':
observations[task_idx,
step, :] = ptu.get_numpy(obs[0, :obs_size])
for episode_idx in range(num_episodes):
running_reward = 0.
for step_idx in range(num_steps_per_episode):
# add distribution parameters to observation - policy is conditioned on posterior
if self.args.policy == 'dqn':
action, value = self.agent.act(obs=obs,
deterministic=True)
else:
action, _, _, log_prob = self.agent.act(
obs=obs,
deterministic=self.args.eval_deterministic,
return_log_prob=True)
# observe reward and next obs
next_obs, reward, done, info = utl.env_step(
self.env, action.squeeze(dim=0))
running_reward += reward.item()
if self.args.policy == 'dqn':
values[task_idx, step] = value.item()
else:
observations[task_idx, step + 1, :] = ptu.get_numpy(
next_obs[0, :obs_size])
log_probs[task_idx, step] = ptu.get_numpy(log_prob[0])
if "is_goal_state" in dir(
self.env.unwrapped
) and self.env.unwrapped.is_goal_state():
success_rate[task_idx] = 1.
# set: obs <- next_obs
obs = next_obs.clone()
step += 1
returns_per_episode[task_idx, episode_idx] = running_reward
if self.args.policy == 'dqn':
return returns_per_episode, success_rate, values
else:
return returns_per_episode, success_rate, log_probs, observations
def log(self, iteration, train_stats):
# --- save models ---
if iteration % self.args.save_interval == 0:
if self.args.save_models:
if self.args.log_tensorboard:
save_path = os.path.join(self.tb_logger.full_output_folder,
'models')
else:
save_path = os.path.join(self.output_dir, 'models')
if not os.path.exists(save_path):
os.mkdir(save_path)
torch.save(
self.agent.state_dict(),
os.path.join(save_path, "agent{0}.pt".format(iteration)))
if hasattr(self.args, 'save_buffer') and self.args.save_buffer:
self.save_buffer()
# evaluate to get more stats
if self.args.policy == 'dqn':
# get stats on train tasks
returns_train, success_rate_train, values = self.evaluate([0])
else:
# get stats on train tasks
returns_train, success_rate_train, log_probs, observations = self.evaluate(
[0])
if self.args.log_tensorboard:
if self.args.policy != 'dqn':
# self.env.reset(0)
# self.tb_logger.writer.add_figure('policy_vis_train/task_0',
# utl_eval.plot_rollouts(observations[0, :], self.env),
# self._n_env_steps_total)
# obs, _, _, _, _ = self.sample_rl_batch(tasks=[0],
# batch_size=self.policy_storage.task_buffers[0].size())
# self.tb_logger.writer.add_figure('state_space_coverage/task_0',
# utl_eval.plot_visited_states(ptu.get_numpy(obs[0][:, :2]), self.env),
# self._n_env_steps_total)
pass
# some metrics
self.tb_logger.writer.add_scalar(
'metrics/successes_in_buffer',
self._successes_in_buffer / self._n_env_steps_total,
self._n_env_steps_total)
if self.args.max_rollouts_per_task > 1:
for episode_idx in range(self.args.max_rollouts_per_task):
self.tb_logger.writer.add_scalar(
'returns_multi_episode/episode_{}'.format(episode_idx +
1),
np.mean(returns_train[:, episode_idx]),
self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'returns_multi_episode/sum',
np.mean(np.sum(returns_train, axis=-1)),
self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'returns_multi_episode/success_rate',
np.mean(success_rate_train), self._n_env_steps_total)
else:
self.tb_logger.writer.add_scalar('returns/returns_mean_train',
np.mean(returns_train),
self._n_env_steps_total)
self.tb_logger.writer.add_scalar('returns/returns_std_train',
np.std(returns_train),
self._n_env_steps_total)
self.tb_logger.writer.add_scalar('returns/success_rate_train',
np.mean(success_rate_train),
self._n_env_steps_total)
# policy
if self.args.policy == 'dqn':
self.tb_logger.writer.add_scalar('policy/value_init',
np.mean(values[:, 0]),
self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'policy/value_halfway',
np.mean(values[:, int(values.shape[-1] / 2)]),
self._n_env_steps_total)
self.tb_logger.writer.add_scalar('policy/value_final',
np.mean(values[:, -1]),
self._n_env_steps_total)
self.tb_logger.writer.add_scalar('policy/exploration_epsilon',
self.agent.eps,
self._n_env_steps_total)
# RL losses
self.tb_logger.writer.add_scalar(
'rl_losses/qf_loss_vs_n_updates', train_stats['qf_loss'],
self._n_rl_update_steps_total)
self.tb_logger.writer.add_scalar(
'rl_losses/qf_loss_vs_n_env_steps', train_stats['qf_loss'],
self._n_env_steps_total)
else:
self.tb_logger.writer.add_scalar('policy/log_prob',
np.mean(log_probs),
self._n_env_steps_total)
self.tb_logger.writer.add_scalar('rl_losses/qf1_loss',
train_stats['qf1_loss'],
self._n_env_steps_total)
self.tb_logger.writer.add_scalar('rl_losses/qf2_loss',
train_stats['qf2_loss'],
self._n_env_steps_total)
self.tb_logger.writer.add_scalar('rl_losses/policy_loss',
train_stats['policy_loss'],
self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'rl_losses/alpha_entropy_loss',
train_stats['alpha_entropy_loss'], self._n_env_steps_total)
# weights and gradients
if self.args.policy == 'dqn':
self.tb_logger.writer.add_scalar(
'weights/q_network',
list(self.agent.qf.parameters())[0].mean(),
self._n_env_steps_total)
if list(self.agent.qf.parameters())[0].grad is not None:
param_list = list(self.agent.qf.parameters())
self.tb_logger.writer.add_scalar(
'gradients/q_network',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'weights/q_target',
list(self.agent.target_qf.parameters())[0].mean(),
self._n_env_steps_total)
if list(self.agent.target_qf.parameters())[0].grad is not None:
param_list = list(self.agent.target_qf.parameters())
self.tb_logger.writer.add_scalar(
'gradients/q_target',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_env_steps_total)
else:
self.tb_logger.writer.add_scalar(
'weights/q1_network',
list(self.agent.qf1.parameters())[0].mean(),
self._n_env_steps_total)
if list(self.agent.qf1.parameters())[0].grad is not None:
param_list = list(self.agent.qf1.parameters())
self.tb_logger.writer.add_scalar(
'gradients/q1_network',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'weights/q1_target',
list(self.agent.qf1_target.parameters())[0].mean(),
self._n_env_steps_total)
if list(self.agent.qf1_target.parameters()
)[0].grad is not None:
param_list = list(self.agent.qf1_target.parameters())
self.tb_logger.writer.add_scalar(
'gradients/q1_target',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'weights/q2_network',
list(self.agent.qf2.parameters())[0].mean(),
self._n_env_steps_total)
if list(self.agent.qf2.parameters())[0].grad is not None:
param_list = list(self.agent.qf2.parameters())
self.tb_logger.writer.add_scalar(
'gradients/q2_network',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'weights/q2_target',
list(self.agent.qf2_target.parameters())[0].mean(),
self._n_env_steps_total)
if list(self.agent.qf2_target.parameters()
)[0].grad is not None:
param_list = list(self.agent.qf2_target.parameters())
self.tb_logger.writer.add_scalar(
'gradients/q2_target',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'weights/policy',
list(self.agent.policy.parameters())[0].mean(),
self._n_env_steps_total)
if list(self.agent.policy.parameters())[0].grad is not None:
param_list = list(self.agent.policy.parameters())
self.tb_logger.writer.add_scalar(
'gradients/policy',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_env_steps_total)
print(
"Iteration -- {}, Success rate -- {:.3f}, Avg. return -- {:.3f}, Elapsed time {:5d}[s]"
.format(iteration, np.mean(success_rate_train),
np.mean(np.sum(returns_train, axis=-1)),
int(time.time() - self._start_time)))
# output to user
# print("Iteration -- {:3d}, Num. RL updates -- {:6d}, Elapsed time {:5d}[s]".
# format(iteration,
# self._n_rl_update_steps_total,
# int(time.time() - self._start_time)))
def training_mode(self, mode):
self.agent.train(mode)
def collect_rollouts(self, num_rollouts, random_actions=False):
'''
:param num_rollouts:
:param random_actions: whether to use policy to sample actions, or randomly sample action space
:return:
'''
for rollout in range(num_rollouts):
obs = ptu.from_numpy(self.env.reset(self.task_idx))
obs = obs.reshape(-1, obs.shape[-1])
done_rollout = False
while not done_rollout:
if random_actions:
if self.args.policy == 'dqn':
action = ptu.FloatTensor([[[
self.env.action_space.sample()
]]]).long() # Sample random action
else:
action = ptu.FloatTensor([
self.env.action_space.sample()
]) # Sample random action
else:
if self.args.policy == 'dqn':
action, _ = self.agent.act(obs=obs) # DQN
else:
action, _, _, _ = self.agent.act(obs=obs) # SAC
# observe reward and next obs
next_obs, reward, done, info = utl.env_step(
self.env, action.squeeze(dim=0))
done_rollout = False if ptu.get_numpy(
done[0][0]) == 0. else True
# add data to policy buffer - (s+, a, r, s'+, term)
term = self.env.unwrapped.is_goal_state(
) if "is_goal_state" in dir(self.env.unwrapped) else False
if self.args.dense_train_sparse_test:
rew_to_buffer = {
rew_type: rew
for rew_type, rew in info.items()
if rew_type.startswith('reward')
}
else:
rew_to_buffer = ptu.get_numpy(reward.squeeze(dim=0))
self.policy_storage.add_sample(
task=self.task_idx,
observation=ptu.get_numpy(obs.squeeze(dim=0)),
action=ptu.get_numpy(action.squeeze(dim=0)),
reward=rew_to_buffer,
terminal=np.array([term], dtype=float),
next_observation=ptu.get_numpy(next_obs.squeeze(dim=0)))
# set: obs <- next_obs
obs = next_obs.clone()
# update statistics
self._n_env_steps_total += 1
if "is_goal_state" in dir(
self.env.unwrapped
) and self.env.unwrapped.is_goal_state(): # count successes
self._successes_in_buffer += 1
self._n_rollouts_total += 1
def sample_rl_batch(self, tasks, batch_size):
''' sample batch of unordered rl training data from a list/array of tasks '''
# this batch consists of transitions sampled randomly from replay buffer
batches = [
ptu.np_to_pytorch_batch(
self.policy_storage.random_batch(task, batch_size))
for task in tasks
]
unpacked = [utl.unpack_batch(batch) for batch in batches]
# group elements together
unpacked = [[x[i] for x in unpacked] for i in range(len(unpacked[0]))]
unpacked = [torch.cat(x, dim=0) for x in unpacked]
return unpacked
def _start_training(self):
self._n_env_steps_total = 0
self._n_rl_update_steps_total = 0
self._n_vae_update_steps_total = 0
self._n_rollouts_total = 0
self._successes_in_buffer = 0
self._start_time = time.time()
def load_model(self, device='cpu', **kwargs):
if "agent_path" in kwargs:
self.agent.load_state_dict(
torch.load(kwargs["agent_path"], map_location=device))
self.training_mode(False)
def save_buffer(self):
size = self.policy_storage.task_buffers[0].size()
np.save(os.path.join(self.output_dir, 'obs'),
self.policy_storage.task_buffers[0]._observations[:size])
np.save(os.path.join(self.output_dir, 'actions'),
self.policy_storage.task_buffers[0]._actions[:size])
if self.args.dense_train_sparse_test:
for reward_type, reward_arr in self.policy_storage.task_buffers[
0]._rewards.items():
np.save(os.path.join(self.output_dir, reward_type),
reward_arr[:size])
else:
np.save(os.path.join(self.output_dir, 'rewards'),
self.policy_storage.task_buffers[0]._rewards[:size])
np.save(os.path.join(self.output_dir, 'next_obs'),
self.policy_storage.task_buffers[0]._next_obs[:size])
np.save(os.path.join(self.output_dir, 'terminals'),
self.policy_storage.task_buffers[0]._terminals[:size])
def initialize_policy(self):
if self.args.policy == 'dqn':
assert self.args.act_space.__class__.__name__ == "Discrete", (
"Can't train DQN with continuous action space!")
q_network = FlattenMlp(input_size=self.args.obs_dim,
output_size=self.args.act_space.n,
hidden_sizes=self.args.dqn_layers)
self.agent = DQN(
q_network,
# optimiser_vae=self.optimizer_vae,
lr=self.args.policy_lr,
gamma=self.args.gamma,
eps_init=self.args.dqn_epsilon_init,
eps_final=self.args.dqn_epsilon_final,
exploration_iters=self.args.dqn_exploration_iters,
tau=self.args.soft_target_tau,
).to(ptu.device)
# elif self.args.policy == 'ddqn':
# assert self.args.act_space.__class__.__name__ == "Discrete", (
# "Can't train DDQN with continuous action space!")
# q_network = FlattenMlp(input_size=self.args.obs_dim,
# output_size=self.args.act_space.n,
# hidden_sizes=self.args.dqn_layers)
# self.agent = DoubleDQN(
# q_network,
# # optimiser_vae=self.optimizer_vae,
# lr=self.args.policy_lr,
# eps_optim=self.args.dqn_eps,
# alpha_optim=self.args.dqn_alpha,
# gamma=self.args.gamma,
# eps_init=self.args.dqn_epsilon_init,
# eps_final=self.args.dqn_epsilon_final,
# exploration_iters=self.args.dqn_exploration_iters,
# tau=self.args.soft_target_tau,
# ).to(ptu.device)
elif self.args.policy == 'sac':
assert self.args.act_space.__class__.__name__ == "Box", (
"Can't train SAC with discrete action space!")
q1_network = FlattenMlp(input_size=self.args.obs_dim +
self.args.action_dim,
output_size=1,
hidden_sizes=self.args.dqn_layers)
q2_network = FlattenMlp(input_size=self.args.obs_dim +
self.args.action_dim,
output_size=1,
hidden_sizes=self.args.dqn_layers)
policy = TanhGaussianPolicy(obs_dim=self.args.obs_dim,
action_dim=self.args.action_dim,
hidden_sizes=self.args.policy_layers)
self.agent = SAC(
policy,
q1_network,
q2_network,
actor_lr=self.args.actor_lr,
critic_lr=self.args.critic_lr,
gamma=self.args.gamma,
tau=self.args.soft_target_tau,
entropy_alpha=self.args.entropy_alpha,
automatic_entropy_tuning=self.args.automatic_entropy_tuning,
alpha_lr=self.args.alpha_lr).to(ptu.device)
else:
raise NotImplementedError
class MetaLearnerSACMER:
"""
Meta-Learner class.
"""
def __init__(self, args):
"""
Seeds everything.
Initialises: logger, environments, policy (+storage +optimiser).
"""
self.args = args
# make sure everything has the same seed
utl.seed(self.args.seed)
# initialize tensorboard logger
if self.args.log_tensorboard:
self.tb_logger = TBLogger(self.args)
# initialise environment
self.env = make_env(self.args.env_name,
self.args.max_rollouts_per_task,
seed=self.args.seed,
n_tasks=self.args.num_tasks)
# unwrapped env to get some info about the environment
unwrapped_env = self.env.unwrapped
# split to train/eval tasks
shuffled_tasks = np.random.permutation(
unwrapped_env.get_all_task_idx())
self.train_tasks = shuffled_tasks[:self.args.num_train_tasks]
if self.args.num_eval_tasks > 0:
self.eval_tasks = shuffled_tasks[-self.args.num_eval_tasks:]
else:
self.eval_tasks = []
# calculate what the maximum length of the trajectories is
args.max_trajectory_len = unwrapped_env._max_episode_steps
args.max_trajectory_len *= self.args.max_rollouts_per_task
self.args.max_trajectory_len = args.max_trajectory_len
# get action / observation dimensions
if isinstance(self.env.action_space, gym.spaces.discrete.Discrete):
self.args.action_dim = 1
else:
self.args.action_dim = self.env.action_space.shape[0]
self.args.obs_dim = self.env.observation_space.shape[0]
self.args.num_states = unwrapped_env.num_states if hasattr(
unwrapped_env, 'num_states') else None
self.args.act_space = self.env.action_space
# initialize policy
self.initialize_policy()
# initialize buffer for RL updates
self.policy_storage = MultiTaskPolicyStorage(
max_replay_buffer_size=int(self.args.policy_buffer_size),
obs_dim=self._get_augmented_obs_dim(),
action_space=self.env.action_space,
tasks=self.train_tasks,
trajectory_len=args.max_trajectory_len,
)
self.current_experience_storage = None
self.args.belief_reward = False # initialize arg to not use belief rewards
def initialize_current_experience_storage(self):
self.current_experience_storage = MultiTaskPolicyStorage(
max_replay_buffer_size=int(self.args.num_tasks_sample *
self.args.num_rollouts_per_iter *
self.args.max_trajectory_len),
obs_dim=self._get_augmented_obs_dim(),
action_space=self.env.action_space,
tasks=self.train_tasks,
trajectory_len=self.args.max_trajectory_len,
)
def initialize_policy(self):
if self.args.policy == 'dqn':
assert self.args.act_space.__class__.__name__ == "Discrete", (
"Can't train DQN with continuous action space!")
q_network = FlattenMlp(input_size=self._get_augmented_obs_dim(),
output_size=self.args.act_space.n,
hidden_sizes=self.args.dqn_layers)
self.agent = DQN(
q_network,
# optimiser_vae=self.optimizer_vae,
lr=self.args.policy_lr,
eps_optim=self.args.dqn_eps,
alpha_optim=self.args.dqn_alpha,
gamma=self.args.gamma,
eps_init=self.args.dqn_epsilon_init,
eps_final=self.args.dqn_epsilon_final,
exploration_iters=self.args.dqn_exploration_iters,
tau=self.args.soft_target_tau,
).to(ptu.device)
elif self.args.policy == 'ddqn':
assert self.args.act_space.__class__.__name__ == "Discrete", (
"Can't train DDQN with continuous action space!")
q_network = FlattenMlp(input_size=self._get_augmented_obs_dim(),
output_size=self.args.act_space.n,
hidden_sizes=self.args.dqn_layers)
self.agent = DoubleDQN(
q_network,
# optimiser_vae=self.optimizer_vae,
lr=self.args.policy_lr,
eps_optim=self.args.dqn_eps,
alpha_optim=self.args.dqn_alpha,
gamma=self.args.gamma,
eps_init=self.args.dqn_epsilon_init,
eps_final=self.args.dqn_epsilon_final,
exploration_iters=self.args.dqn_exploration_iters,
tau=self.args.soft_target_tau,
).to(ptu.device)
elif self.args.policy == 'sac':
assert self.args.act_space.__class__.__name__ == "Box", (
"Can't train SAC with discrete action space!")
q1_network = FlattenMlp(input_size=self._get_augmented_obs_dim() +
self.args.action_dim,
output_size=1,
hidden_sizes=self.args.dqn_layers)
q2_network = FlattenMlp(input_size=self._get_augmented_obs_dim() +
self.args.action_dim,
output_size=1,
hidden_sizes=self.args.dqn_layers)
policy = TanhGaussianPolicy(obs_dim=self._get_augmented_obs_dim(),
action_dim=self.args.action_dim,
hidden_sizes=self.args.policy_layers)
self.agent = SAC(
policy,
q1_network,
q2_network,
actor_lr=self.args.actor_lr,
critic_lr=self.args.critic_lr,
gamma=self.args.gamma,
tau=self.args.soft_target_tau,
entropy_alpha=self.args.entropy_alpha,
automatic_entropy_tuning=self.args.automatic_entropy_tuning,
alpha_lr=self.args.alpha_lr).to(ptu.device)
else:
raise NotImplementedError
def train(self):
"""
meta-training loop
"""
self._start_training()
for iter_ in range(self.args.num_iters):
self.training_mode(True)
# switch to belief reward
if self.args.switch_to_belief_reward is not None and iter_ >= self.args.switch_to_belief_reward:
self.args.belief_reward = True
if iter_ == 0:
print('Collecting initial pool of data..')
for task in self.train_tasks:
self.task_idx = task
self.env.reset_task(idx=task)
# self.collect_rollouts(num_rollouts=self.args.num_init_rollouts_pool)
self.collect_rollouts(
num_rollouts=self.args.num_init_rollouts_pool,
random_actions=True)
print('Done!')
# collect data from subset of train tasks
tasks_to_collect = [
self.train_tasks[np.random.randint(len(self.train_tasks))]
for _ in range(self.args.num_tasks_sample)
]
self.initialize_current_experience_storage()
for i in range(self.args.num_tasks_sample):
task = tasks_to_collect[i]
self.task_idx = task
self.env.reset_task(idx=task)
self.collect_rollouts(
num_rollouts=self.args.num_rollouts_per_iter)
# update
indices = np.random.choice(self.train_tasks, self.args.meta_batch)
train_stats = self.update(indices,
current_task_indices=tasks_to_collect)
self.training_mode(False)
if self.args.policy == 'dqn':
self.agent.set_exploration_parameter(iter_ + 1)
# evaluate and log
if (iter_ + 1) % self.args.log_interval == 0:
self.log(iter_ + 1, train_stats)
def update(self, tasks, current_task_indices=[]):
'''
Meta-update
:param tasks: list/array of task indices. perform update based on the tasks
:param current_task_indices: indices that the last rollout collected
:return:
'''
# --- RL TRAINING ---
rl_losses_agg = {}
prev_qf1 = copy.deepcopy(self.agent.qf1).to(ptu.device)
prev_qf2 = copy.deepcopy(self.agent.qf2).to(ptu.device)
prev_qf1_target = copy.deepcopy(self.agent.qf1_target).to(ptu.device)
prev_qf2_target = copy.deepcopy(self.agent.qf2_target).to(ptu.device)
prev_policy = copy.deepcopy(self.agent.policy).to(ptu.device)
# prev_alpha = copy.deepcopy(self.agent.log_alpha_entropy) # not relevant when alpha is const
prev_nets = [
prev_qf1, prev_qf2, prev_qf1_target, prev_qf2_target, prev_policy
]
updates_from_current_experience = np.random.choice(
self.args.rl_updates_per_iter, self.args.mer_s, replace=False)
for update in range(self.args.rl_updates_per_iter):
# sample random RL batch
obs, actions, rewards, next_obs, terms = self.sample_rl_batch(
tasks if update not in updates_from_current_experience else
current_task_indices,
self.args.batch_size,
is_sample_from_current_experience=(
update in updates_from_current_experience))
# flatten out task dimension
t, b, _ = obs.size()
obs = obs.view(t * b, -1)
actions = actions.view(t * b, -1)
rewards = rewards.view(t * b, -1)
next_obs = next_obs.view(t * b, -1)
terms = terms.view(t * b, -1)
# RL update
rl_losses = self.agent.update(obs, actions, rewards, next_obs,
terms)
for k, v in rl_losses.items():
if update == 0: # first iterate - create list
rl_losses_agg[k] = [v]
else: # append values
rl_losses_agg[k].append(v)
# reptile update
new_nets = [
self.agent.qf1, self.agent.qf2, self.agent.qf1_target,
self.agent.qf2_target, self.agent.policy
]
for new_net, prev_net in zip(new_nets, prev_nets):
ptu.soft_update_from_to(new_net, prev_net,
self.args.mer_gamma_policy)
ptu.copy_model_params_from_to(prev_net, new_net)
# take mean
for k in rl_losses_agg:
rl_losses_agg[k] = np.mean(rl_losses_agg[k])
self._n_rl_update_steps_total += self.args.rl_updates_per_iter
train_stats = {
**rl_losses_agg,
}
return train_stats
def evaluate(self, tasks):
num_episodes = self.args.max_rollouts_per_task
num_steps_per_episode = self.env.unwrapped._max_episode_steps
returns_per_episode = np.zeros((len(tasks), num_episodes))
success_rate = np.zeros(len(tasks))
if self.args.policy == 'dqn':
values = np.zeros((len(tasks), self.args.max_trajectory_len))
else:
rewards = np.zeros((len(tasks), self.args.max_trajectory_len))
obs_size = self.env.unwrapped.observation_space.shape[0]
observations = np.zeros(
(len(tasks), self.args.max_trajectory_len + 1, obs_size))
for task_idx, task in enumerate(tasks):
obs = ptu.from_numpy(self.env.reset(task))
obs = obs.reshape(-1, obs.shape[-1])
step = 0
if self.args.policy == 'dqn':
raise NotImplementedError
else:
observations[task_idx,
step, :] = ptu.get_numpy(obs[0, :obs_size])
for episode_idx in range(num_episodes):
running_reward = 0.
for step_idx in range(num_steps_per_episode):
# add distribution parameters to observation - policy is conditioned on posterior
augmented_obs = self.get_augmented_obs(obs=obs)
if self.args.policy == 'dqn':
action, value = self.agent.act(obs=augmented_obs,
deterministic=True)
else:
action, _, _, log_prob = self.agent.act(
obs=augmented_obs,
deterministic=self.args.eval_deterministic,
return_log_prob=True)
# observe reward and next obs
next_obs, reward, done, info = utl.env_step(
self.env, action.squeeze(dim=0))
running_reward += reward.item()
if self.args.policy == 'dqn':
values[task_idx, step] = value.item()
else:
rewards[task_idx, step] = reward.item()
observations[task_idx, step + 1, :] = ptu.get_numpy(
next_obs[0, :obs_size])
if "is_goal_state" in dir(
self.env.unwrapped
) and self.env.unwrapped.is_goal_state():
success_rate[task_idx] = 1.
# set: obs <- next_obs
obs = next_obs.clone()
step += 1
returns_per_episode[task_idx, episode_idx] = running_reward
if self.args.policy == 'dqn':
return returns_per_episode, success_rate, values,
else:
return returns_per_episode, success_rate, observations, rewards,
def log(self, iteration, train_stats):
# --- save models ---
if iteration % self.args.save_interval == 0:
save_path = os.path.join(self.tb_logger.full_output_folder,
'models')
if not os.path.exists(save_path):
os.mkdir(save_path)
torch.save(
self.agent.state_dict(),
os.path.join(save_path, "agent{0}.pt".format(iteration)))
# evaluate to get more stats
if self.args.policy == 'dqn':
# get stats on train tasks
returns_train, success_rate_train, values = self.evaluate(
self.train_tasks)
else:
# get stats on train tasks
returns_train, success_rate_train, observations, rewards_train = self.evaluate(
self.train_tasks[:len(self.eval_tasks)])
returns_eval, success_rate_eval, observations_eval, rewards_eval = self.evaluate(
self.eval_tasks)
if self.args.log_tensorboard:
# some metrics
self.tb_logger.writer.add_scalar(
'metrics/successes_in_buffer',
self._successes_in_buffer / self._n_env_steps_total,
self._n_env_steps_total)
if self.args.max_rollouts_per_task > 1:
for episode_idx in range(self.args.max_rollouts_per_task):
self.tb_logger.writer.add_scalar(
'returns_multi_episode/episode_{}'.format(episode_idx +
1),
np.mean(returns_train[:, episode_idx]),
self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'returns_multi_episode/sum',
np.mean(np.sum(returns_train, axis=-1)),
self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'returns_multi_episode/success_rate',
np.mean(success_rate_train), self._n_env_steps_total)
if self.args.policy != 'dqn':
self.tb_logger.writer.add_scalar(
'returns_multi_episode/sum_eval',
np.mean(np.sum(returns_eval, axis=-1)),
self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'returns_multi_episode/success_rate_eval',
np.mean(success_rate_eval), self._n_env_steps_total)
else:
# self.tb_logger.writer.add_scalar('returns/returns_mean', np.mean(returns),
# self._n_env_steps_total)
# self.tb_logger.writer.add_scalar('returns/returns_std', np.std(returns),
# self._n_env_steps_total)
self.tb_logger.writer.add_scalar('returns/returns_mean_train',
np.mean(returns_train),
self._n_env_steps_total)
self.tb_logger.writer.add_scalar('returns/returns_std_train',
np.std(returns_train),
self._n_env_steps_total)
# self.tb_logger.writer.add_scalar('returns/success_rate', np.mean(success_rate),
# self._n_env_steps_total)
self.tb_logger.writer.add_scalar('returns/success_rate_train',
np.mean(success_rate_train),
self._n_env_steps_total)
# policy
if self.args.policy == 'dqn':
self.tb_logger.writer.add_scalar('policy/value_init',
np.mean(values[:, 0]),
self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'policy/value_halfway',
np.mean(values[:, int(values.shape[-1] / 2)]),
self._n_env_steps_total)
self.tb_logger.writer.add_scalar('policy/value_final',
np.mean(values[:, -1]),
self._n_env_steps_total)
self.tb_logger.writer.add_scalar('policy/exploration_epsilon',
self.agent.eps,
self._n_env_steps_total)
# RL losses
self.tb_logger.writer.add_scalar(
'rl_losses/qf_loss_vs_n_updates', train_stats['qf_loss'],
self._n_rl_update_steps_total)
self.tb_logger.writer.add_scalar(
'rl_losses/qf_loss_vs_n_env_steps', train_stats['qf_loss'],
self._n_env_steps_total)
else:
self.tb_logger.writer.add_scalar('rl_losses/qf1_loss',
train_stats['qf1_loss'],
self._n_env_steps_total)
self.tb_logger.writer.add_scalar('rl_losses/qf2_loss',
train_stats['qf2_loss'],
self._n_env_steps_total)
self.tb_logger.writer.add_scalar('rl_losses/policy_loss',
train_stats['policy_loss'],
self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'rl_losses/alpha_loss', train_stats['alpha_entropy_loss'],
self._n_env_steps_total)
# weights and gradients
if self.args.policy == 'dqn':
self.tb_logger.writer.add_scalar(
'weights/q_network',
list(self.agent.qf.parameters())[0].mean(),
self._n_env_steps_total)
if list(self.agent.qf.parameters())[0].grad is not None:
param_list = list(self.agent.qf.parameters())
self.tb_logger.writer.add_scalar(
'gradients/q_network',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'weights/q_target',
list(self.agent.target_qf.parameters())[0].mean(),
self._n_env_steps_total)
if list(self.agent.target_qf.parameters())[0].grad is not None:
param_list = list(self.agent.target_qf.parameters())
self.tb_logger.writer.add_scalar(
'gradients/q_target',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_env_steps_total)
else:
self.tb_logger.writer.add_scalar(
'weights/q1_network',
list(self.agent.qf1.parameters())[0].mean(),
self._n_env_steps_total)
if list(self.agent.qf1.parameters())[0].grad is not None:
param_list = list(self.agent.qf1.parameters())
self.tb_logger.writer.add_scalar(
'gradients/q1_network',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'weights/q1_target',
list(self.agent.qf1_target.parameters())[0].mean(),
self._n_env_steps_total)
if list(self.agent.qf1_target.parameters()
)[0].grad is not None:
param_list = list(self.agent.qf1_target.parameters())
self.tb_logger.writer.add_scalar(
'gradients/q1_target',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'weights/q2_network',
list(self.agent.qf2.parameters())[0].mean(),
self._n_env_steps_total)
if list(self.agent.qf2.parameters())[0].grad is not None:
param_list = list(self.agent.qf2.parameters())
self.tb_logger.writer.add_scalar(
'gradients/q2_network',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'weights/q2_target',
list(self.agent.qf2_target.parameters())[0].mean(),
self._n_env_steps_total)
if list(self.agent.qf2_target.parameters()
)[0].grad is not None:
param_list = list(self.agent.qf2_target.parameters())
self.tb_logger.writer.add_scalar(
'gradients/q2_target',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_env_steps_total)
self.tb_logger.writer.add_scalar(
'weights/policy',
list(self.agent.policy.parameters())[0].mean(),
self._n_env_steps_total)
if list(self.agent.policy.parameters())[0].grad is not None:
param_list = list(self.agent.policy.parameters())
self.tb_logger.writer.add_scalar(
'gradients/policy',
sum([
param_list[i].grad.mean()
for i in range(len(param_list))
]), self._n_env_steps_total)
#log iteration
self.tb_logger.writer.add_scalar('iteration', iteration,
self._n_env_steps_total)
# output to user
# print("Iteration -- {:3d}, Num. RL updates -- {:6d}, Elapsed time {:5d}[s]".
# format(iteration,
# self._n_rl_update_steps_total,
# int(time.time() - self._start_time)))
print(
"Iteration -- {}, Success rate train -- {:.3f}, Success rate eval.-- {:.3f}, "
"Avg. return train -- {:.3f}, Avg. return eval. -- {:.3f}, Elapsed time {:5d}[s]"
.format(iteration, np.mean(success_rate_train),
np.mean(success_rate_eval),
np.mean(np.sum(returns_train, axis=-1)),
np.mean(np.sum(returns_eval, axis=-1)),
int(time.time() - self._start_time)))
def training_mode(self, mode):
# policy
self.agent.train(mode)
def collect_rollouts(self, num_rollouts, random_actions=False):
'''
:param num_rollouts:
:param random_actions: whether to use policy to sample actions, or randomly sample action space
:return:
'''
for rollout in range(num_rollouts):
obs = ptu.from_numpy(self.env.reset(self.task_idx))
obs = obs.reshape(-1, obs.shape[-1])
done_rollout = False
# self.policy_storage.reset_running_episode(self.task_idx)
# if self.args.fixed_latent_params:
# assert 2 ** self.args.task_embedding_size >= self.args.num_tasks
# task_mean = ptu.FloatTensor(utl.vertices(self.args.task_embedding_size)[self.task_idx])
# task_logvar = -2. * ptu.ones_like(task_logvar) # arbitrary negative enough number
# add distribution parameters to observation - policy is conditioned on posterior
augmented_obs = self.get_augmented_obs(obs=obs)
while not done_rollout:
if random_actions:
if self.args.policy == 'dqn':
action = ptu.FloatTensor([[
self.env.action_space.sample()
]]).type(torch.long) # Sample random action
else:
action = ptu.FloatTensor(
[self.env.action_space.sample()])
else:
if self.args.policy == 'dqn':
action, _ = self.agent.act(obs=augmented_obs) # DQN
else:
action, _, _, _ = self.agent.act(
obs=augmented_obs) # SAC
# observe reward and next obs
next_obs, reward, done, info = utl.env_step(
self.env, action.squeeze(dim=0))
done_rollout = False if ptu.get_numpy(
done[0][0]) == 0. else True
# get augmented next obs
augmented_next_obs = self.get_augmented_obs(obs=next_obs)
# add data to policy buffer - (s+, a, r, s'+, term)
term = self.env.unwrapped.is_goal_state(
) if "is_goal_state" in dir(self.env.unwrapped) else False
self.policy_storage.add_sample(
task=self.task_idx,
observation=ptu.get_numpy(augmented_obs.squeeze(dim=0)),
action=ptu.get_numpy(action.squeeze(dim=0)),
reward=ptu.get_numpy(reward.squeeze(dim=0)),
terminal=np.array([term], dtype=float),
next_observation=ptu.get_numpy(
augmented_next_obs.squeeze(dim=0)))
if not random_actions:
self.current_experience_storage.add_sample(
task=self.task_idx,
observation=ptu.get_numpy(
augmented_obs.squeeze(dim=0)),
action=ptu.get_numpy(action.squeeze(dim=0)),
reward=ptu.get_numpy(reward.squeeze(dim=0)),
terminal=np.array([term], dtype=float),
next_observation=ptu.get_numpy(
augmented_next_obs.squeeze(dim=0)))
# set: obs <- next_obs
obs = next_obs.clone()
augmented_obs = augmented_next_obs.clone()
# update statistics
self._n_env_steps_total += 1
if "is_goal_state" in dir(
self.env.unwrapped
) and self.env.unwrapped.is_goal_state(): # count successes
self._successes_in_buffer += 1
self._n_rollouts_total += 1
def get_augmented_obs(self, obs):
augmented_obs = obs.clone()
return augmented_obs
def _get_augmented_obs_dim(self):
dim = utl.get_dim(self.env.observation_space)
return dim
def sample_rl_batch(self,
tasks,
batch_size,
is_sample_from_current_experience=False):
''' sample batch of unordered rl training data from a list/array of tasks '''
# this batch consists of transitions sampled randomly from replay buffer
if is_sample_from_current_experience:
batches = [
ptu.np_to_pytorch_batch(
self.current_experience_storage.random_batch(
task, batch_size)) for task in tasks
]
else:
batches = [
ptu.np_to_pytorch_batch(
self.policy_storage.random_batch(task, batch_size))
for task in tasks
]
unpacked = [utl.unpack_batch(batch) for batch in batches]
# group elements together
unpacked = [[x[i] for x in unpacked] for i in range(len(unpacked[0]))]
unpacked = [torch.cat(x, dim=0) for x in unpacked]
return unpacked
def _start_training(self):
self._n_env_steps_total = 0
self._n_rl_update_steps_total = 0
self._n_rollouts_total = 0
self._successes_in_buffer = 0
self._start_time = time.time()
def load_model(self, device='cpu', **kwargs):
if "agent_path" in kwargs:
self.agent.load_state_dict(
torch.load(kwargs["agent_path"], map_location=device))
self.training_mode(False)
