class MetaRLAlgorithm(metaclass=abc.ABCMeta):
def __init__(
self,
env,
agent,
train_tasks,
eval_tasks,
meta_batch=64,
num_iterations=100,
num_train_steps_per_itr=1000,
num_initial_steps=100,
num_tasks_sample=100,
num_steps_prior=100,
num_steps_posterior=100,
num_extra_rl_steps_posterior=100,
num_evals=10,
num_steps_per_eval=1000,
batch_size=1024,
embedding_batch_size=1024,
embedding_mini_batch_size=1024,
max_path_length=1000,
discount=0.99,
replay_buffer_size=1000000,
reward_scale=1,
num_exp_traj_eval=1,
update_post_train=1,
eval_deterministic=True,
render=False,
save_replay_buffer=False,
save_algorithm=False,
save_environment=False,
render_eval_paths=False,
dump_eval_paths=False,
plotter=None,
dyna=False,
dyna_num_train_itr=50,
dyna_num_train_steps_per_itr=50,
dyna_tandem_train=True,
dyna_n_layers=3,
dyna_hidden_layer_size=64,
dyna_learning_rate=1e-3,
):
"""
:param env: training env
:param agent: agent that is conditioned on a latent variable z that rl_algorithm is responsible for feeding in
:param train_tasks: list of tasks used for training
:param eval_tasks: list of tasks used for eval
see default experiment config file for descriptions of the rest of the arguments
"""
self.env = env
self.agent = agent
self.exploration_agent = agent # Can potentially use a different policy purely for exploration rather than also solving tasks, currently not being used
self.train_tasks = train_tasks
self.eval_tasks = eval_tasks
self.meta_batch = meta_batch
self.num_iterations = num_iterations
self.num_train_steps_per_itr = num_train_steps_per_itr
self.num_initial_steps = num_initial_steps
self.num_tasks_sample = num_tasks_sample
self.num_steps_prior = num_steps_prior
self.num_steps_posterior = num_steps_posterior
self.num_extra_rl_steps_posterior = num_extra_rl_steps_posterior
self.num_evals = num_evals
self.num_steps_per_eval = num_steps_per_eval
self.batch_size = batch_size
self.embedding_batch_size = embedding_batch_size
self.embedding_mini_batch_size = embedding_mini_batch_size
self.max_path_length = max_path_length
self.discount = discount
self.replay_buffer_size = replay_buffer_size
self.reward_scale = reward_scale
self.update_post_train = update_post_train
self.num_exp_traj_eval = num_exp_traj_eval
self.eval_deterministic = eval_deterministic
self.render = render
self.save_replay_buffer = save_replay_buffer
self.save_algorithm = save_algorithm
self.save_environment = save_environment
self.eval_statistics = None
self.render_eval_paths = render_eval_paths
self.dump_eval_paths = dump_eval_paths
self.plotter = plotter
self.dyna = dyna
self.dyna_num_train_itr = dyna_num_train_itr
self.dyna_num_train_steps_per_itr = dyna_num_train_steps_per_itr
self.dyna_tandem_train = dyna_tandem_train
self.dyna_n_layers = dyna_n_layers
self.dyna_hidden_layer_size = dyna_hidden_layer_size
self.dyna_learning_rate = dyna_learning_rate
if dyna:
self.sampler = DynamicsSampler(
env=env,
policy=agent,
max_path_length=self.max_path_length,
num_train_itr=dyna_num_train_itr,
num_train_steps_per_itr=dyna_num_train_steps_per_itr,
tandem_train=dyna_tandem_train,
n_layers=dyna_n_layers,
hidden_layer_size=dyna_hidden_layer_size,
learning_rate=dyna_learning_rate,
)
else:
self.sampler = InPlacePathSampler(
env=env,
policy=agent,
max_path_length=self.max_path_length,
)
# separate replay buffers for
# - training RL update
# - training encoder update
self.replay_buffer = MultiTaskReplayBuffer(
self.replay_buffer_size,
env,
self.train_tasks,
)
self.enc_replay_buffer = MultiTaskReplayBuffer(
self.replay_buffer_size,
env,
self.train_tasks,
)
self._n_env_steps_total = 0
self._n_train_steps_total = 0
self._n_rollouts_total = 0
self._do_train_time = 0
self._epoch_start_time = None
self._algo_start_time = None
self._old_table_keys = None
self._current_path_builder = PathBuilder()
self._exploration_paths = []
def make_exploration_policy(self, policy):
return policy
def make_eval_policy(self, policy):
return policy
def sample_task(self, is_eval=False):
'''
sample task randomly
'''
if is_eval:
idx = np.random.randint(len(self.eval_tasks))
else:
idx = np.random.randint(len(self.train_tasks))
return idx
def train(self):
'''
meta-training loop
'''
self.pretrain()
params = self.get_epoch_snapshot(-1)
logger.save_itr_params(-1, params)
gt.reset()
gt.set_def_unique(False)
self._current_path_builder = PathBuilder()
# at each iteration, we first collect data from tasks, perform meta-updates, then try to evaluate
for it_ in gt.timed_for(
range(self.num_iterations),
save_itrs=True,
):
self._start_epoch(it_)
self.training_mode(True)
if it_ == 0:
print('collecting initial pool of data for train and eval')
# temp for evaluating
for idx in self.train_tasks:
self.task_idx = idx
self.env.reset_task(idx)
self.collect_data(self.num_initial_steps, 1, np.inf)
# Sample data from train tasks.
for i in range(self.num_tasks_sample):
idx = np.random.randint(len(self.train_tasks))
self.task_idx = idx
self.env.reset_task(idx)
self.enc_replay_buffer.task_buffers[idx].clear()
# collect some trajectories with z ~ prior
if self.num_steps_prior > 0:
self.collect_data(self.num_steps_prior, 1, np.inf)
# collect some trajectories with z ~ posterior
if self.num_steps_posterior > 0:
self.collect_data(self.num_steps_posterior, 1,
self.update_post_train)
# even if encoder is trained only on samples from the prior, the policy needs to learn to handle z ~ posterior
if self.num_extra_rl_steps_posterior > 0:
self.collect_data(self.num_extra_rl_steps_posterior,
1,
self.update_post_train,
add_to_enc_buffer=False)
# Sample train tasks and compute gradient updates on parameters.
for train_step in range(self.num_train_steps_per_itr):
indices = np.random.choice(self.train_tasks, self.meta_batch)
self._do_training(indices)
self._n_train_steps_total += 1
gt.stamp('train')
self.training_mode(False)
# eval
self._try_to_eval(it_)
gt.stamp('eval')
self._end_epoch()
def pretrain(self):
"""
Do anything before the main training phase.
"""
pass
def collect_data(self,
num_samples,
resample_z_rate,
update_posterior_rate,
add_to_enc_buffer=True):
'''
get trajectories from current env in batch mode with given policy
collect complete trajectories until the number of collected transitions >= num_samples
:param agent: policy to rollout
:param num_samples: total number of transitions to sample
:param resample_z_rate: how often to resample latent context z (in units of trajectories)
:param update_posterior_rate: how often to update q(z | c) from which z is sampled (in units of trajectories)
:param add_to_enc_buffer: whether to add collected data to encoder replay buffer
'''
# start from the prior
self.agent.clear_z()
num_transitions = 0
while num_transitions < num_samples:
paths, n_samples = self.sampler.obtain_samples(
max_samples=num_samples - num_transitions,
max_trajs=update_posterior_rate,
accum_context=False,
resample=resample_z_rate,
testing=False)
num_transitions += n_samples
self.replay_buffer.add_paths(self.task_idx, paths)
if add_to_enc_buffer:
self.enc_replay_buffer.add_paths(self.task_idx, paths)
if update_posterior_rate != np.inf:
context = self.sample_context(self.task_idx)
self.agent.infer_posterior(context)
self._n_env_steps_total += num_transitions
gt.stamp('sample')
def _try_to_eval(self, epoch):
logger.save_extra_data(self.get_extra_data_to_save(epoch))
if self._can_evaluate():
self.evaluate(epoch)
params = self.get_epoch_snapshot(epoch)
logger.save_itr_params(epoch, params)
table_keys = logger.get_table_key_set()
if self._old_table_keys is not None:
assert table_keys == self._old_table_keys, (
"Table keys cannot change from iteration to iteration.")
self._old_table_keys = table_keys
logger.record_tabular(
"Number of train steps total",
self._n_train_steps_total,
)
logger.record_tabular(
"Number of env steps total",
self._n_env_steps_total,
)
logger.record_tabular(
"Number of rollouts total",
self._n_rollouts_total,
)
times_itrs = gt.get_times().stamps.itrs
train_time = times_itrs['train'][-1]
sample_time = times_itrs['sample'][-1]
eval_time = times_itrs['eval'][-1] if epoch > 0 else 0
epoch_time = train_time + sample_time + eval_time
total_time = gt.get_times().total
logger.record_tabular('Train Time (s)', train_time)
logger.record_tabular('(Previous) Eval Time (s)', eval_time)
logger.record_tabular('Sample Time (s)', sample_time)
logger.record_tabular('Epoch Time (s)', epoch_time)
logger.record_tabular('Total Train Time (s)', total_time)
logger.record_tabular("Epoch", epoch)
logger.dump_tabular(with_prefix=False, with_timestamp=False)
else:
logger.log("Skipping eval for now.")
def _can_evaluate(self):
"""
One annoying thing about the logger table is that the keys at each
iteration need to be the exact same. So unless you can compute
everything, skip evaluation.
A common example for why you might want to skip evaluation is that at
the beginning of training, you may not have enough data for a
validation and training set.
:return:
"""
# eval collects its own context, so can eval any time
return True
def _can_train(self):
return all([
self.replay_buffer.num_steps_can_sample(idx) >= self.batch_size
for idx in self.train_tasks
])
def _get_action_and_info(self, agent, observation):
"""
Get an action to take in the environment.
:param observation:
:return:
"""
agent.set_num_steps_total(self._n_env_steps_total)
return agent.get_action(observation, )
def _start_epoch(self, epoch):
self._epoch_start_time = time.time()
self._exploration_paths = []
self._do_train_time = 0
logger.push_prefix('Iteration #%d | ' % epoch)
def _end_epoch(self):
logger.log("Epoch Duration: {0}".format(time.time() -
self._epoch_start_time))
logger.log("Started Training: {0}".format(self._can_train()))
logger.pop_prefix()
##### Snapshotting utils #####
def get_epoch_snapshot(self, epoch):
data_to_save = dict(
epoch=epoch,
exploration_policy=self.exploration_policy,
)
if self.save_environment:
data_to_save['env'] = self.training_env
return data_to_save
def get_extra_data_to_save(self, epoch):
"""
Save things that shouldn't be saved every snapshot but rather
overwritten every time.
:param epoch:
:return:
"""
if self.render:
self.training_env.render(close=True)
data_to_save = dict(epoch=epoch, )
if self.save_environment:
data_to_save['env'] = self.training_env
if self.save_replay_buffer:
data_to_save['replay_buffer'] = self.replay_buffer
if self.save_algorithm:
data_to_save['algorithm'] = self
return data_to_save
def collect_paths(self, idx, epoch, run):
self.task_idx = idx
self.env.reset_task(idx)
self.agent.clear_z()
paths = []
num_transitions = 0
num_trajs = 0
while num_transitions < self.num_steps_per_eval:
path, num = self.sampler.obtain_samples(
deterministic=self.eval_deterministic,
max_samples=self.num_steps_per_eval - num_transitions,
max_trajs=1,
accum_context=True,
testing=True)
paths += path
num_transitions += num
num_trajs += 1
if num_trajs >= self.num_exp_traj_eval:
self.agent.infer_posterior(self.agent.context)
if self.sparse_rewards:
for p in paths:
sparse_rewards = np.stack(
e['sparse_reward'] for e in p['env_infos']).reshape(-1, 1)
p['rewards'] = sparse_rewards
goal = self.env._goal
for path in paths:
path['goal'] = goal # goal
# save the paths for visualization, only useful for point mass
if self.dump_eval_paths:
logger.save_extra_data(
paths,
path='eval_trajectories/task{}-epoch{}-run{}'.format(
idx, epoch, run))
return paths
def _do_eval(self, indices, epoch):
final_returns = []
online_returns = []
for idx in indices:
all_rets = []
for r in range(self.num_evals):
paths = self.collect_paths(idx, epoch, r)
all_rets.append(
[eval_util.get_average_returns([p]) for p in paths])
final_returns.append(np.mean([a[-1] for a in all_rets]))
# record online returns for the first n trajectories
n = min([len(a) for a in all_rets])
all_rets = [a[:n] for a in all_rets]
all_rets = np.mean(np.stack(all_rets),
axis=0) # avg return per nth rollout
online_returns.append(all_rets)
n = min([len(t) for t in online_returns])
online_returns = [t[:n] for t in online_returns]
return final_returns, online_returns
def evaluate(self, epoch):
if self.eval_statistics is None:
self.eval_statistics = OrderedDict()
### sample trajectories from prior for debugging / visualization
if self.dump_eval_paths:
# 100 arbitrarily chosen for visualizations of point_robot trajectories
# just want stochasticity of z, not the policy
self.agent.clear_z()
prior_paths, _ = self.sampler.obtain_samples(
deterministic=self.eval_deterministic,
max_samples=self.max_path_length * 20,
accum_context=False,
resample=1,
testing=True)
logger.save_extra_data(
prior_paths,
path='eval_trajectories/prior-epoch{}'.format(epoch))
### train tasks
# eval on a subset of train tasks for speed
indices = np.random.choice(self.train_tasks, len(self.eval_tasks))
eval_util.dprint('evaluating on {} train tasks'.format(len(indices)))
### eval train tasks with posterior sampled from the training replay buffer
train_returns = []
for idx in indices:
self.task_idx = idx
self.env.reset_task(idx)
paths = []
for _ in range(self.num_steps_per_eval // self.max_path_length):
context = self.sample_context(idx)
self.agent.infer_posterior(context)
p, _ = self.sampler.obtain_samples(
deterministic=self.eval_deterministic,
max_samples=self.max_path_length,
accum_context=False,
max_trajs=1,
resample=np.inf,
testing=True)
paths += p
if self.sparse_rewards:
for p in paths:
sparse_rewards = np.stack(e['sparse_reward']
for e in p['env_infos']).reshape(
-1, 1)
p['rewards'] = sparse_rewards
train_returns.append(eval_util.get_average_returns(paths))
train_returns = np.mean(train_returns)
### eval train tasks with on-policy data to match eval of test tasks
train_final_returns, train_online_returns = self._do_eval(
indices, epoch)
eval_util.dprint('train online returns')
eval_util.dprint(train_online_returns)
### test tasks
eval_util.dprint('evaluating on {} test tasks'.format(
len(self.eval_tasks)))
test_final_returns, test_online_returns = self._do_eval(
self.eval_tasks, epoch)
eval_util.dprint('test online returns')
eval_util.dprint(test_online_returns)
# save the final posterior
self.agent.log_diagnostics(self.eval_statistics)
if hasattr(self.env, "log_diagnostics"):
self.env.log_diagnostics(paths, prefix=None)
avg_train_return = np.mean(train_final_returns)
avg_test_return = np.mean(test_final_returns)
avg_train_online_return = np.mean(np.stack(train_online_returns),
axis=0)
avg_test_online_return = np.mean(np.stack(test_online_returns), axis=0)
self.eval_statistics[
'AverageTrainReturn_all_train_tasks'] = train_returns
self.eval_statistics[
'AverageReturn_all_train_tasks'] = avg_train_return
self.eval_statistics['AverageReturn_all_test_tasks'] = avg_test_return
logger.save_extra_data(avg_train_online_return,
path='online-train-epoch{}'.format(epoch))
logger.save_extra_data(avg_test_online_return,
path='online-test-epoch{}'.format(epoch))
for key, value in self.eval_statistics.items():
logger.record_tabular(key, value)
self.eval_statistics = None
if self.render_eval_paths:
self.env.render_paths(paths)
if self.plotter:
self.plotter.draw()
@abc.abstractmethod
def training_mode(self, mode):
"""
Set training mode to `mode`.
:param mode: If True, training will happen (e.g. set the dropout
probabilities to not all ones).
"""
pass
@abc.abstractmethod
def _do_training(self):
"""
Perform some update, e.g. perform one gradient step.
:return:
"""
pass
class OfflineMetaRLAlgorithm(metaclass=abc.ABCMeta):
def __init__(self,
env,
agent,
train_tasks,
eval_tasks,
goal_radius,
eval_deterministic=True,
render=False,
render_eval_paths=False,
plotter=None,
**kwargs):
"""
:param env: training env
:param agent: agent that is conditioned on a latent variable z that rl_algorithm is responsible for feeding in
:param train_tasks: list of tasks used for training
:param eval_tasks: list of tasks used for eval
:param goal_radius: reward threshold for defining sparse rewards
see default experiment config file for descriptions of the rest of the arguments
"""
self.env = env
self.agent = agent
self.train_tasks = train_tasks
self.eval_tasks = eval_tasks
self.goal_radius = goal_radius
self.meta_batch = kwargs['meta_batch']
self.batch_size = kwargs['batch_size']
self.num_iterations = kwargs['num_iterations']
self.num_train_steps_per_itr = kwargs['num_train_steps_per_itr']
self.num_initial_steps = kwargs['num_initial_steps']
self.num_tasks_sample = kwargs['num_tasks_sample']
self.num_steps_prior = kwargs['num_steps_prior']
self.num_steps_posterior = kwargs['num_steps_posterior']
self.num_extra_rl_steps_posterior = kwargs[
'num_extra_rl_steps_posterior']
self.num_evals = kwargs['num_evals']
self.num_steps_per_eval = kwargs['num_steps_per_eval']
self.embedding_batch_size = kwargs['embedding_batch_size']
self.embedding_mini_batch_size = kwargs['embedding_mini_batch_size']
self.max_path_length = kwargs['max_path_length']
self.discount = kwargs['discount']
self.replay_buffer_size = kwargs['replay_buffer_size']
self.reward_scale = kwargs['reward_scale']
self.update_post_train = kwargs['update_post_train']
self.num_exp_traj_eval = kwargs['num_exp_traj_eval']
self.save_replay_buffer = kwargs['save_replay_buffer']
self.save_algorithm = kwargs['save_algorithm']
self.save_environment = kwargs['save_environment']
self.dump_eval_paths = kwargs['dump_eval_paths']
self.data_dir = kwargs['data_dir']
self.train_epoch = kwargs['train_epoch']
self.eval_epoch = kwargs['eval_epoch']
self.sample = kwargs['sample']
self.n_trj = kwargs['n_trj']
self.allow_eval = kwargs['allow_eval']
self.mb_replace = kwargs['mb_replace']
self.eval_deterministic = eval_deterministic
self.render = render
self.eval_statistics = None
self.render_eval_paths = render_eval_paths
self.plotter = plotter
self.train_buffer = MultiTaskReplayBuffer(self.replay_buffer_size, env,
self.train_tasks,
self.goal_radius)
self.eval_buffer = MultiTaskReplayBuffer(self.replay_buffer_size, env,
self.eval_tasks,
self.goal_radius)
self.replay_buffer = MultiTaskReplayBuffer(self.replay_buffer_size,
env, self.train_tasks,
self.goal_radius)
self.enc_replay_buffer = MultiTaskReplayBuffer(self.replay_buffer_size,
env, self.train_tasks,
self.goal_radius)
# offline sampler which samples from the train/eval buffer
self.offline_sampler = OfflineInPlacePathSampler(
env=env, policy=agent, max_path_length=self.max_path_length)
# online sampler for evaluation (if collect on-policy context, for offline context, use self.offline_sampler)
self.sampler = InPlacePathSampler(env=env,
policy=agent,
max_path_length=self.max_path_length)
self._n_env_steps_total = 0
self._n_train_steps_total = 0
self._n_rollouts_total = 0
self._do_train_time = 0
self._epoch_start_time = None
self._algo_start_time = None
self._old_table_keys = None
self._current_path_builder = PathBuilder()
self._exploration_paths = []
self.init_buffer()
def init_buffer(self):
train_trj_paths = []
eval_trj_paths = []
# trj entry format: [obs, action, reward, new_obs]
if self.sample:
for n in range(self.n_trj):
if self.train_epoch is None:
train_trj_paths += glob.glob(
os.path.join(self.data_dir, "goal_idx*",
"trj_evalsample%d_step*.npy" % (n)))
else:
train_trj_paths += glob.glob(
os.path.join(
self.data_dir, "goal_idx*",
"trj_evalsample%d_step%d.npy" %
(n, self.train_epoch)))
if self.eval_epoch is None:
eval_trj_paths += glob.glob(
os.path.join(self.data_dir, "goal_idx*",
"trj_evalsample%d_step*.npy" % (n)))
else:
eval_trj_paths += glob.glob(
os.path.join(
self.data_dir, "goal_idx*",
"trj_evalsample%d_step%d.npy" %
(n, self.eval_epoch)))
else:
if self.train_epoch is None:
train_trj_paths = glob.glob(
os.path.join(self.data_dir, "goal_idx*",
"trj_eval[0-%d]_step*.npy") % (self.n_trj))
else:
train_trj_paths = glob.glob(
os.path.join(
self.data_dir, "goal_idx*",
"trj_eval[0-%d]_step%d.npy" %
(self.n_trj, self.train_epoch)))
if self.eval_epoch is None:
eval_trj_paths = glob.glob(
os.path.join(self.data_dir, "goal_idx*",
"trj_eval[0-%d]_step*.npy") % (self.n_trj))
else:
eval_trj_paths = glob.glob(
os.path.join(
self.data_dir, "goal_idx*",
"trj_eval[0-%d]_step%d.npy" %
(self.n_trj, self.test_epoch)))
train_paths = [
train_trj_path for train_trj_path in train_trj_paths
if int(train_trj_path.split('/')[-2].split('goal_idx')[-1]) in
self.train_tasks
]
train_task_idxs = [
int(train_trj_path.split('/')[-2].split('goal_idx')[-1])
for train_trj_path in train_trj_paths
if int(train_trj_path.split('/')[-2].split('goal_idx')[-1]) in
self.train_tasks
]
eval_paths = [
eval_trj_path for eval_trj_path in eval_trj_paths
if int(eval_trj_path.split('/')[-2].split('goal_idx')[-1]) in
self.eval_tasks
]
eval_task_idxs = [
int(eval_trj_path.split('/')[-2].split('goal_idx')[-1])
for eval_trj_path in eval_trj_paths
if int(eval_trj_path.split('/')[-2].split('goal_idx')[-1]) in
self.eval_tasks
]
obs_train_lst = []
action_train_lst = []
reward_train_lst = []
next_obs_train_lst = []
terminal_train_lst = []
task_train_lst = []
obs_eval_lst = []
action_eval_lst = []
reward_eval_lst = []
next_obs_eval_lst = []
terminal_eval_lst = []
task_eval_lst = []
for train_path, train_task_idx in zip(train_paths, train_task_idxs):
trj_npy = np.load(train_path, allow_pickle=True)
obs_train_lst += list(trj_npy[:, 0])
action_train_lst += list(trj_npy[:, 1])
reward_train_lst += list(trj_npy[:, 2])
next_obs_train_lst += list(trj_npy[:, 3])
terminal = [0 for _ in range(trj_npy.shape[0])]
terminal[-1] = 1
terminal_train_lst += terminal
task_train = [train_task_idx for _ in range(trj_npy.shape[0])]
task_train_lst += task_train
for eval_path, eval_task_idx in zip(eval_paths, eval_task_idxs):
trj_npy = np.load(eval_path, allow_pickle=True)
obs_eval_lst += list(trj_npy[:, 0])
action_eval_lst += list(trj_npy[:, 1])
reward_eval_lst += list(trj_npy[:, 2])
next_obs_eval_lst += list(trj_npy[:, 3])
terminal = [0 for _ in range(trj_npy.shape[0])]
terminal[-1] = 1
terminal_eval_lst += terminal
task_eval = [eval_task_idx for _ in range(trj_npy.shape[0])]
task_eval_lst += task_eval
# load training buffer
for i, (
task_train,
obs,
action,
reward,
next_obs,
terminal,
) in enumerate(
zip(
task_train_lst,
obs_train_lst,
action_train_lst,
reward_train_lst,
next_obs_train_lst,
terminal_train_lst,
)):
self.train_buffer.add_sample(
task_train,
obs,
action,
reward,
terminal,
next_obs,
**{'env_info': {}},
)
# load evaluation buffer
for i, (
task_eval,
obs,
action,
reward,
next_obs,
terminal,
) in enumerate(
zip(
task_eval_lst,
obs_eval_lst,
action_eval_lst,
reward_eval_lst,
next_obs_eval_lst,
terminal_eval_lst,
)):
self.eval_buffer.add_sample(
task_eval,
obs,
action,
reward,
terminal,
next_obs,
**{'env_info': {}},
)
def _try_to_eval(self, epoch):
#logger.save_extra_data(self.get_extra_data_to_save(epoch))
if self._can_evaluate():
self.evaluate(epoch)
#params = self.get_epoch_snapshot(epoch)
#logger.save_itr_params(epoch, params)
table_keys = logger.get_table_key_set()
if self._old_table_keys is not None:
assert table_keys == self._old_table_keys, (
"Table keys cannot change from iteration to iteration.")
self._old_table_keys = table_keys
logger.record_tabular("Number of train steps total",
self._n_train_steps_total)
logger.record_tabular("Number of env steps total",
self._n_env_steps_total)
logger.record_tabular("Number of rollouts total",
self._n_rollouts_total)
times_itrs = gt.get_times().stamps.itrs
train_time = times_itrs['train'][-1]
sample_time = times_itrs['sample'][-1]
eval_time = times_itrs['eval'][-1] if epoch > 0 else 0
epoch_time = train_time + sample_time + eval_time
total_time = gt.get_times().total
logger.record_tabular('Train Time (s)', train_time)
logger.record_tabular('(Previous) Eval Time (s)', eval_time)
logger.record_tabular('Sample Time (s)', sample_time)
logger.record_tabular('Epoch Time (s)', epoch_time)
logger.record_tabular('Total Train Time (s)', total_time)
logger.record_tabular("Epoch", epoch)
logger.dump_tabular(with_prefix=False, with_timestamp=False)
else:
logger.log("Skipping eval for now.")
def _can_evaluate(self):
"""
One annoying thing about the logger table is that the keys at each
iteration need to be the exact same. So unless you can compute
everything, skip evaluation.
A common example for why you might want to skip evaluation is that at
the beginning of training, you may not have enough data for a
validation and training set.
:return:
"""
# eval collects its own context, so can eval any time
return True
def _can_train(self):
return all([
self.replay_buffer.num_steps_can_sample(idx) >= self.batch_size
for idx in self.train_tasks
])
def _get_action_and_info(self, agent, observation):
"""
Get an action to take in the environment.
:param observation:
:return:
"""
agent.set_num_steps_total(self._n_env_steps_total)
return agent.get_action(observation, )
def _start_epoch(self, epoch):
self._epoch_start_time = time.time()
self._exploration_paths = []
self._do_train_time = 0
logger.push_prefix('Iteration #%d | ' % epoch)
def _end_epoch(self):
logger.log("Epoch Duration: {0}".format(time.time() -
self._epoch_start_time))
logger.log("Started Training: {0}".format(self._can_train()))
logger.pop_prefix()
##### Snapshotting utils #####
def get_epoch_snapshot(self, epoch):
data_to_save = dict(
epoch=epoch,
exploration_policy=self.exploration_policy,
)
if self.save_environment:
data_to_save['env'] = self.training_env
return data_to_save
def get_extra_data_to_save(self, epoch):
"""
Save things that shouldn't be saved every snapshot but rather
overwritten every time.
:param epoch:
:return:
"""
if self.render:
self.training_env.render(close=True)
data_to_save = dict(epoch=epoch, )
if self.save_environment:
data_to_save['env'] = self.training_env
if self.save_replay_buffer:
data_to_save['replay_buffer'] = self.replay_buffer
if self.save_algorithm:
data_to_save['algorithm'] = self
return data_to_save
def _do_eval(self, indices, epoch, buffer):
final_returns = []
online_returns = []
for idx in indices:
all_rets = []
for r in range(self.num_evals):
paths = self.collect_paths(idx, epoch, r, buffer)
all_rets.append(
[eval_util.get_average_returns([p]) for p in paths])
final_returns.append(np.mean([a[-1] for a in all_rets]))
# record online returns for the first n trajectories
n = min([len(a) for a in all_rets])
all_rets = [a[:n] for a in all_rets]
all_rets = np.mean(np.stack(all_rets),
axis=0) # avg return per nth rollout
online_returns.append(all_rets)
n = min([len(t) for t in online_returns])
online_returns = [t[:n] for t in online_returns]
return final_returns, online_returns
def test(self, log_dir, end_point=-1):
assert os.path.exists(log_dir)
gt.reset()
gt.set_def_unique(False)
self._current_path_builder = PathBuilder()
# at each iteration, we first collect data from tasks, perform meta-updates, then try to evaluate
for it_ in gt.timed_for(range(self.num_iterations), save_itrs=True):
self._start_epoch(it_)
if it_ == 0:
print('collecting initial pool of data for test')
# temp for evaluating
for idx in self.train_tasks:
self.task_idx = idx
self.env.reset_task(idx)
self.collect_data(self.num_initial_steps,
1,
np.inf,
buffer=self.train_buffer)
# Sample data from train tasks.
for i in range(self.num_tasks_sample):
idx = np.random.choice(self.train_tasks, 1)[0]
self.task_idx = idx
self.env.reset_task(idx)
self.enc_replay_buffer.task_buffers[idx].clear()
# collect some trajectories with z ~ prior
if self.num_steps_prior > 0:
self.collect_data(self.num_steps_prior,
1,
np.inf,
buffer=self.train_buffer)
# collect some trajectories with z ~ posterior
if self.num_steps_posterior > 0:
self.collect_data(self.num_steps_posterior,
1,
self.update_post_train,
buffer=self.train_buffer)
# even if encoder is trained only on samples from the prior, the policy needs to learn to handle z ~ posterior
if self.num_extra_rl_steps_posterior > 0:
self.collect_data(self.num_extra_rl_steps_posterior,
1,
self.update_post_train,
buffer=self.train_buffer,
add_to_enc_buffer=False)
print([
self.replay_buffer.task_buffers[idx]._size
for idx in self.train_tasks
])
print([
self.enc_replay_buffer.task_buffers[idx]._size
for idx in self.train_tasks
])
for train_step in range(self.num_train_steps_per_itr):
self._n_train_steps_total += 1
gt.stamp('train')
# eval
self.training_mode(False)
if it_ % 5 == 0 and it_ > end_point:
status = self.load_epoch_model(it_, log_dir)
if status:
self._try_to_eval(it_)
gt.stamp('eval')
self._end_epoch()
def train(self):
'''
meta-training loop
'''
params = self.get_epoch_snapshot(-1)
logger.save_itr_params(-1, params)
gt.reset()
gt.set_def_unique(False)
self._current_path_builder = PathBuilder()
# at each iteration, we first collect data from tasks, perform meta-updates, then try to evaluate
for it_ in gt.timed_for(range(self.num_iterations), save_itrs=True):
self._start_epoch(it_)
self.training_mode(True)
if it_ == 0:
print('collecting initial pool of data for train and eval')
# temp for evaluating
for idx in self.train_tasks:
self.task_idx = idx
self.env.reset_task(idx)
self.collect_data(self.num_initial_steps,
1,
np.inf,
buffer=self.train_buffer)
# Sample data from train tasks.
for i in range(self.num_tasks_sample):
idx = np.random.choice(self.train_tasks, 1)[0]
self.task_idx = idx
self.env.reset_task(idx)
self.enc_replay_buffer.task_buffers[idx].clear()
# collect some trajectories with z ~ prior
if self.num_steps_prior > 0:
self.collect_data(self.num_steps_prior,
1,
np.inf,
buffer=self.train_buffer)
# collect some trajectories with z ~ posterior
if self.num_steps_posterior > 0:
self.collect_data(self.num_steps_posterior,
1,
self.update_post_train,
buffer=self.train_buffer)
# even if encoder is trained only on samples from the prior, the policy needs to learn to handle z ~ posterior
if self.num_extra_rl_steps_posterior > 0:
self.collect_data(self.num_extra_rl_steps_posterior,
1,
self.update_post_train,
buffer=self.train_buffer,
add_to_enc_buffer=False)
indices_lst = []
z_means_lst = []
z_vars_lst = []
# Sample train tasks and compute gradient updates on parameters.
for train_step in range(self.num_train_steps_per_itr):
indices = np.random.choice(self.train_tasks,
self.meta_batch,
replace=self.mb_replace)
z_means, z_vars = self._do_training(indices, zloss=True)
indices_lst.append(indices)
z_means_lst.append(z_means)
z_vars_lst.append(z_vars)
self._n_train_steps_total += 1
indices = np.concatenate(indices_lst)
z_means = np.concatenate(z_means_lst)
z_vars = np.concatenate(z_vars_lst)
data_dict = self.data_dict(indices, z_means, z_vars)
logger.save_itr_data(it_, **data_dict)
gt.stamp('train')
self.training_mode(False)
# eval
params = self.get_epoch_snapshot(it_)
logger.save_itr_params(it_, params)
if self.allow_eval:
logger.save_extra_data(self.get_extra_data_to_save(it_))
self._try_to_eval(it_)
gt.stamp('eval')
self._end_epoch()
def data_dict(self, indices, z_means, z_vars):
data_dict = {}
data_dict['task_idx'] = indices
for i in range(z_means.shape[1]):
data_dict['z_means%d' % i] = list(z_means[:, i])
for i in range(z_vars.shape[1]):
data_dict['z_vars%d' % i] = list(z_vars[:, i])
return data_dict
def evaluate(self, epoch):
if self.eval_statistics is None:
self.eval_statistics = OrderedDict()
### sample trajectories from prior for debugging / visualization
if self.dump_eval_paths:
# 100 arbitrarily chosen for visualizations of point_robot trajectories
# just want stochasticity of z, not the policy
self.agent.clear_z()
prior_paths, _ = self.offline_sampler.obtain_samples(
buffer=self.train_buffer,
deterministic=self.eval_deterministic,
max_samples=self.max_path_length * 20,
accum_context=False,
resample=1)
logger.save_extra_data(
prior_paths,
path='eval_trajectories/prior-epoch{}'.format(epoch))
### train tasks
# eval on a subset of train tasks for speed
# {}-dir envs
if len(self.train_tasks) == 2 and len(self.eval_tasks) == 2:
indices = self.train_tasks
eval_util.dprint('evaluating on {} train tasks'.format(
len(indices)))
### eval train tasks with posterior sampled from the training replay buffer
train_returns = []
for idx in indices:
self.task_idx = idx
self.env.reset_task(idx)
paths = []
print(self.num_steps_per_eval, self.max_path_length)
for _ in range(self.num_steps_per_eval //
self.max_path_length):
context = self.sample_context(idx)
self.agent.infer_posterior(context, idx)
p, _ = self.offline_sampler.obtain_samples(
buffer=self.train_buffer,
deterministic=self.eval_deterministic,
max_samples=self.max_path_length,
accum_context=False,
max_trajs=1,
resample=np.inf)
paths += p
if self.sparse_rewards:
for p in paths:
sparse_rewards = np.stack(
e['sparse_reward']
for e in p['env_infos']).reshape(-1, 1)
p['rewards'] = sparse_rewards
train_returns.append(eval_util.get_average_returns(paths))
### eval train tasks with on-policy data to match eval of test tasks
train_final_returns, train_online_returns = self._do_eval(
indices, epoch, buffer=self.train_buffer)
eval_util.dprint('train online returns')
eval_util.dprint(train_online_returns)
### test tasks
eval_util.dprint('evaluating on {} test tasks'.format(
len(self.eval_tasks)))
test_final_returns, test_online_returns = self._do_eval(
self.eval_tasks, epoch, buffer=self.eval_buffer)
eval_util.dprint('test online returns')
eval_util.dprint(test_online_returns)
# save the final posterior
self.agent.log_diagnostics(self.eval_statistics)
if hasattr(self.env, "log_diagnostics"):
self.env.log_diagnostics(paths, prefix=None)
avg_train_online_return = np.mean(np.stack(train_online_returns),
axis=0)
avg_test_online_return = np.mean(np.stack(test_online_returns),
axis=0)
for i in indices:
self.eval_statistics[
f'AverageTrainReturn_train_task{i}'] = train_returns[i]
self.eval_statistics[
f'AverageReturn_all_train_task{i}'] = train_final_returns[
i]
self.eval_statistics[
f'AverageReturn_all_test_tasks{i}'] = test_final_returns[i]
# non {}-dir envs
else:
indices = np.random.choice(self.train_tasks, len(self.eval_tasks))
eval_util.dprint('evaluating on {} train tasks'.format(
len(indices)))
### eval train tasks with posterior sampled from the training replay buffer
train_returns = []
for idx in indices:
self.task_idx = idx
self.env.reset_task(idx)
paths = []
for _ in range(self.num_steps_per_eval //
self.max_path_length):
context = self.sample_context(idx)
self.agent.infer_posterior(context, idx)
p, _ = self.offline_sampler.obtain_samples(
buffer=self.train_buffer,
deterministic=self.eval_deterministic,
max_samples=self.max_path_length,
accum_context=False,
max_trajs=1,
resample=np.inf)
paths += p
if self.sparse_rewards:
for p in paths:
sparse_rewards = np.stack(
e['sparse_reward']
for e in p['env_infos']).reshape(-1, 1)
p['rewards'] = sparse_rewards
train_returns.append(eval_util.get_average_returns(paths))
train_returns = np.mean(train_returns)
### eval train tasks with on-policy data to match eval of test tasks
train_final_returns, train_online_returns = self._do_eval(
indices, epoch, buffer=self.train_buffer)
eval_util.dprint('train online returns')
eval_util.dprint(train_online_returns)
### test tasks
eval_util.dprint('evaluating on {} test tasks'.format(
len(self.eval_tasks)))
test_final_returns, test_online_returns = self._do_eval(
self.eval_tasks, epoch, buffer=self.eval_buffer)
eval_util.dprint('test online returns')
eval_util.dprint(test_online_returns)
# save the final posterior
self.agent.log_diagnostics(self.eval_statistics)
if hasattr(self.env, "log_diagnostics"):
self.env.log_diagnostics(paths, prefix=None)
avg_train_return = np.mean(train_final_returns)
avg_test_return = np.mean(test_final_returns)
avg_train_online_return = np.mean(np.stack(train_online_returns),
axis=0)
avg_test_online_return = np.mean(np.stack(test_online_returns),
axis=0)
self.eval_statistics[
'AverageTrainReturn_all_train_tasks'] = train_returns
self.eval_statistics[
'AverageReturn_all_train_tasks'] = avg_train_return
self.eval_statistics[
'AverageReturn_all_test_tasks'] = avg_test_return
self.loss['train_returns'] = train_returns
self.loss['avg_train_return'] = avg_train_return
self.loss['avg_test_return'] = avg_test_return
self.loss['avg_train_online_return'] = np.mean(
avg_train_online_return)
self.loss['avg_test_online_return'] = np.mean(
avg_test_online_return)
logger.save_extra_data(avg_train_online_return,
path='online-train-epoch{}'.format(epoch))
logger.save_extra_data(avg_test_online_return,
path='online-test-epoch{}'.format(epoch))
for key, value in self.eval_statistics.items():
logger.record_tabular(key, value)
self.eval_statistics = None
if self.render_eval_paths:
self.env.render_paths(paths)
if self.plotter:
self.plotter.draw()
def collect_paths(self, idx, epoch, run, buffer):
self.task_idx = idx
self.env.reset_task(idx)
self.agent.clear_z()
paths = []
num_transitions = 0
# num_trajs = 0
while num_transitions < self.num_steps_per_eval:
path, num = self.offline_sampler.obtain_samples(
buffer=buffer,
deterministic=self.eval_deterministic,
max_samples=self.num_steps_per_eval - num_transitions,
max_trajs=1,
accum_context=True,
rollout=True)
paths += path
num_transitions += num
if self.sparse_rewards:
for p in paths:
sparse_rewards = np.stack(
e['sparse_reward'] for e in p['env_infos']).reshape(-1, 1)
p['rewards'] = sparse_rewards
goal = self.env._goal
for path in paths:
path['goal'] = goal # goal
# save the paths for visualization, only useful for point mass
if self.dump_eval_paths:
logger.save_extra_data(
paths,
path='eval_trajectories/task{}-epoch{}-run{}'.format(
idx, epoch, run))
return paths
def collect_data(self,
num_samples,
resample_z_rate,
update_posterior_rate,
buffer,
add_to_enc_buffer=True):
'''
get trajectories from current env in batch mode with given policy
collect complete trajectories until the number of collected transitions >= num_samples
:param agent: policy to rollout
:param num_samples: total number of transitions to sample
:param resample_z_rate: how often to resample latent context z (in units of trajectories)
:param update_posterior_rate: how often to update q(z | c) from which z is sampled (in units of trajectories)
:param add_to_enc_buffer: whether to add collected data to encoder replay buffer
'''
# start from the prior
self.agent.clear_z()
num_transitions = 0
while num_transitions < num_samples:
paths, n_samples = self.offline_sampler.obtain_samples(
buffer=buffer,
max_samples=num_samples - num_transitions,
max_trajs=update_posterior_rate,
accum_context=False,
resample=resample_z_rate,
rollout=False)
num_transitions += n_samples
self.replay_buffer.add_paths(self.task_idx, paths)
if add_to_enc_buffer:
self.enc_replay_buffer.add_paths(self.task_idx, paths)
if update_posterior_rate != np.inf:
context = self.sample_context(self.task_idx)
self.agent.infer_posterior(context,
task_indices=np.array(
[self.task_idx]))
self._n_env_steps_total += num_transitions
gt.stamp('sample')
@abc.abstractmethod
def training_mode(self, mode):
"""
Set training mode to `mode`.
:param mode: If True, training will happen (e.g. set the dropout
probabilities to not all ones).
"""
pass
@abc.abstractmethod
def _do_training(self):
"""
Perform some update, e.g. perform one gradient step.
:return:
"""
pass
class MetaRLAlgorithm(metaclass=abc.ABCMeta):
def __init__(
self,
env,
policy,
train_tasks,
eval_tasks,
meta_batch=64,
num_iterations=100,
num_train_steps_per_itr=1000,
num_tasks_sample=100,
num_steps_per_task=100,
num_evals=10,
num_steps_per_eval=1000,
batch_size=1024,
embedding_batch_size=1024,
embedding_mini_batch_size=1024,
max_path_length=1000,
discount=0.99,
replay_buffer_size=1000000, #1000000,
reward_scale=1,
train_embedding_source='posterior_only',
eval_embedding_source='initial_pool',
eval_deterministic=True,
render=False,
save_replay_buffer=False,
save_algorithm=False,
save_environment=False,
obs_emb_dim=0):
"""
Base class for Meta RL Algorithms
:param env: training env
:param policy: policy that is conditioned on a latent variable z that rl_algorithm is responsible for feeding in
:param train_tasks: list of tasks used for training
:param eval_tasks: list of tasks used for eval
:param meta_batch: number of tasks used for meta-update
:param num_iterations: number of meta-updates taken
:param num_train_steps_per_itr: number of meta-updates performed per iteration
:param num_tasks_sample: number of train tasks to sample to collect data for
:param num_steps_per_task: number of transitions to collect per task
:param num_evals: number of independent evaluation runs, with separate task encodings
:param num_steps_per_eval: number of transitions to sample for evaluation
:param batch_size: size of batches used to compute RL update
:param embedding_batch_size: size of batches used to compute embedding
:param embedding_mini_batch_size: size of batch used for encoder update
:param max_path_length: max episode length
:param discount:
:param replay_buffer_size: max replay buffer size
:param reward_scale:
:param render:
:param save_replay_buffer:
:param save_algorithm:
:param save_environment:
"""
self.env = env
self.policy = policy
self.exploration_policy = policy # Can potentially use a different policy purely for exploration rather than also solving tasks, currently not being used
self.train_tasks = train_tasks
self.eval_tasks = eval_tasks
self.meta_batch = meta_batch
self.num_iterations = num_iterations
self.num_train_steps_per_itr = num_train_steps_per_itr
self.num_tasks_sample = num_tasks_sample
self.num_steps_per_task = num_steps_per_task
self.num_evals = num_evals
self.num_steps_per_eval = num_steps_per_eval
self.batch_size = batch_size
self.embedding_batch_size = embedding_batch_size
self.embedding_mini_batch_size = embedding_mini_batch_size
self.max_path_length = max_path_length
self.discount = discount
self.replay_buffer_size = min(
int(replay_buffer_size / (len(train_tasks))), 1000)
self.reward_scale = reward_scale
self.train_embedding_source = train_embedding_source
self.eval_embedding_source = eval_embedding_source # TODO: add options for computing embeddings on train tasks too
self.eval_deterministic = eval_deterministic
self.render = render
self.save_replay_buffer = save_replay_buffer
self.save_algorithm = save_algorithm
self.save_environment = save_environment
self.eval_sampler = InPlacePathSampler(
env=env,
policy=policy,
max_samples=self.num_steps_per_eval,
max_path_length=self.max_path_length,
)
# separate replay buffers for
# - training RL update
# - training encoder update
# - testing encoder
self.replay_buffer = MultiTaskReplayBuffer(self.replay_buffer_size,
env,
self.train_tasks,
state_dim=obs_emb_dim)
self.enc_replay_buffer = MultiTaskReplayBuffer(self.replay_buffer_size,
env,
self.train_tasks,
state_dim=obs_emb_dim)
self.eval_enc_replay_buffer = MultiTaskReplayBuffer(
self.replay_buffer_size,
env,
self.eval_tasks,
state_dim=obs_emb_dim)
self._n_env_steps_total = 0
self._n_train_steps_total = 0
self._n_rollouts_total = 0
self._do_train_time = 0
self._epoch_start_time = None
self._algo_start_time = None
self._old_table_keys = None
self._current_path_builder = PathBuilder()
self._exploration_paths = []
def make_exploration_policy(self, policy):
return policy
def make_eval_policy(self, policy):
return policy
def sample_task(self, is_eval=False):
'''
sample task randomly
'''
if is_eval:
idx = np.random.randint(len(self.eval_tasks))
else:
idx = np.random.randint(len(self.train_tasks))
return idx
def train(self):
'''
meta-training loop
'''
self.pretrain()
params = self.get_epoch_snapshot(-1)
logger.save_itr_params(-1, params)
gt.reset()
gt.set_def_unique(False)
self._current_path_builder = PathBuilder()
self.train_obs = self._start_new_rollout()
# at each iteration, we first collect data from tasks, perform meta-updates, then try to evaluate
for it_ in gt.timed_for(
range(self.num_iterations),
save_itrs=True,
):
self._start_epoch(it_)
self.training_mode(True)
if it_ == 0:
print('collecting initial pool of data for train and eval')
# temp for evaluating
for idx in self.train_tasks:
print('train task', idx)
self.task_idx = idx
self.env.reset_task(idx)
self.collect_data_sampling_from_prior(
num_samples=self.max_path_length * 10,
resample_z_every_n=self.max_path_length,
eval_task=False)
"""
for idx in self.eval_tasks:
self.task_idx = idx
self.env.reset_task(idx)
# TODO: make number of initial trajectories a parameter
self.collect_data_sampling_from_prior(num_samples=self.max_path_length * 20,
resample_z_every_n=self.max_path_length,
eval_task=True)
"""
# Sample data from train tasks.
for i in range(self.num_tasks_sample):
idx = np.random.randint(len(self.train_tasks))
self.task_idx = idx
self.env.reset_task(idx)
# TODO: there may be more permutations of sampling/adding to encoding buffer we may wish to try
if self.train_embedding_source == 'initial_pool':
# embeddings are computed using only the initial pool of data
# sample data from posterior to train RL algorithm
self.collect_data_from_task_posterior(
idx=idx,
num_samples=self.num_steps_per_task,
add_to_enc_buffer=False)
elif self.train_embedding_source == 'posterior_only':
self.collect_data_from_task_posterior(
idx=idx,
num_samples=self.num_steps_per_task,
eval_task=False,
add_to_enc_buffer=True)
elif self.train_embedding_source == 'online_exploration_trajectories':
# embeddings are computed using only data collected using the prior
# sample data from posterior to train RL algorithm
self.enc_replay_buffer.task_buffers[idx].clear()
# resamples using current policy, conditioned on prior
self.collect_data_sampling_from_prior(
num_samples=self.num_steps_per_task,
resample_z_every_n=self.max_path_length,
add_to_enc_buffer=True)
self.env.reset_task(idx)
self.collect_data_from_task_posterior(
idx=idx,
num_samples=self.num_steps_per_task,
add_to_enc_buffer=False,
viz=True)
elif self.train_embedding_source == 'online_on_policy_trajectories':
# sample from prior, then sample more from the posterior
# embeddings computed from both prior and posterior data
self.enc_replay_buffer.task_buffers[idx].clear()
self.collect_data_online(
idx=idx,
num_samples=self.num_steps_per_task,
add_to_enc_buffer=True)
else:
raise Exception(
"Invalid option for computing train embedding {}".
format(self.train_embedding_source))
# Sample train tasks and compute gradient updates on parameters.
for train_step in range(self.num_train_steps_per_itr):
indices = np.random.choice(self.train_tasks, self.meta_batch)
self._do_training(indices, train_step)
self._n_train_steps_total += 1
gt.stamp('train')
#self.training_mode(False)
# eval
self._try_to_eval(it_)
gt.stamp('eval')
self._end_epoch()
def pretrain(self):
"""
Do anything before the main training phase.
"""
pass
def sample_z_from_prior(self):
"""
Samples z from the prior distribution, which can be either a delta function at 0 or a standard Gaussian
depending on whether we use the information bottleneck.
:return: latent z as a Numpy array
"""
pass
def sample_z_from_posterior(self, idx, eval_task):
"""
Samples z from the posterior distribution given data from task idx, where data comes from the encoding buffer
:param idx: task idx from which to compute the posterior from
:param eval_task: whether or not the task is an eval task
:return: latent z as a Numpy array
"""
pass
# TODO: maybe find a better name for resample_z_every_n?
def collect_data_sampling_from_prior(self,
num_samples=1,
resample_z_every_n=None,
eval_task=False,
add_to_enc_buffer=True):
# do not resample z if resample_z_every_n is None
if resample_z_every_n is None:
self.policy.clear_z()
self.collect_data(self.policy,
num_samples=num_samples,
eval_task=eval_task,
add_to_enc_buffer=add_to_enc_buffer)
else:
# collects more data in batches of resample_z_every_n until done
while num_samples > 0:
self.collect_data_sampling_from_prior(
num_samples=min(resample_z_every_n, num_samples),
resample_z_every_n=None,
eval_task=eval_task,
add_to_enc_buffer=add_to_enc_buffer)
num_samples -= resample_z_every_n
def collect_data_from_task_posterior(self,
idx,
num_samples=1,
resample_z_every_n=None,
eval_task=False,
add_to_enc_buffer=True,
viz=False):
# do not resample z if resample_z_every_n is None
if resample_z_every_n is None:
self.sample_z_from_posterior(idx, eval_task=eval_task)
self.collect_data(self.policy,
num_samples=num_samples,
eval_task=eval_task,
add_to_enc_buffer=add_to_enc_buffer,
viz=viz)
else:
# collects more data in batches of resample_z_every_n until done
while num_samples > 0:
self.collect_data_from_task_posterior(
idx=idx,
num_samples=min(resample_z_every_n, num_samples),
resample_z_every_n=None,
eval_task=eval_task,
add_to_enc_buffer=add_to_enc_buffer,
viz=viz)
num_samples -= resample_z_every_n
# split number of prior and posterior samples
def collect_data_online(self,
idx,
num_samples,
eval_task=False,
add_to_enc_buffer=True):
self.collect_data_sampling_from_prior(
num_samples=num_samples,
resample_z_every_n=self.max_path_length,
eval_task=eval_task,
add_to_enc_buffer=True)
self.env.reset_task(idx)
self.collect_data_from_task_posterior(
idx=idx,
num_samples=num_samples,
resample_z_every_n=self.max_path_length,
eval_task=eval_task,
add_to_enc_buffer=add_to_enc_buffer,
viz=True)
# TODO: since switching tasks now resets the environment, we are not correctly handling episodes terminating
# correctly. We also aren't using the episodes anywhere, but we should probably change this to make it gather paths
# until we have more samples than num_samples, to make sure every episode cleanly terminates when intended.
# @profile
def collect_data(self,
agent,
num_samples=1,
max_resets=None,
eval_task=False,
add_to_enc_buffer=True,
viz=False):
'''
collect data from current env in batch mode
with given policy
'''
images = []
# if num_samples == 50:
# import pdb; pdb.set_trace()
env_time = self.env.time
rews = []
terms = []
n_resets = 0
for _ in range(num_samples):
action, agent_info = self._get_action_and_info(
agent, self.train_obs)
if self.render:
self.env.render()
next_ob, raw_reward, terminal, env_info = (self.env.step(action))
if viz:
images.append(next_ob)
# vis.image(next_ob[-1])
reward = raw_reward
rews += [reward]
terms += [terminal]
terminal = np.array([terminal])
reward = np.array([reward])
self._handle_step(
self.task_idx,
np.concatenate(
[self.train_obs.flatten()[None], agent_info['obs_emb']],
axis=-1),
action,
reward,
np.concatenate([
next_ob.flatten()[None],
torch.zeros(agent_info['obs_emb'].shape)
],
axis=-1),
terminal,
eval_task=eval_task,
add_to_enc_buffer=add_to_enc_buffer,
agent_info=agent_info,
env_info=env_info,
)
# TODO USE masking here to handle the terminal episodes
# print(len(self._current_path_builder))
if terminal or len(
self._current_path_builder) >= self.max_path_length:
self._handle_rollout_ending(eval_task=eval_task)
self.train_obs = self._start_new_rollout()
n_resets += 1
if _ + self.max_path_length > num_samples - 1:
break
if max_resets is not None and n_resets > max_resets:
break
else:
# print((next_ob - self.train_obs).sum())
# self.train_obs = None
self.train_obs = next_ob
if viz and np.random.random() < 0.3:
# import pdb; pdb.set_trace()
vis.images(np.stack(images)[:, -1:])
vis.line(np.array([rews, terms]).T,
opts=dict(width=400, height=320))
vis.text('', opts=dict(width=10000, height=5))
# vis.video(np.stack(images))
if not eval_task:
self._n_env_steps_total += num_samples
gt.stamp('sample')
def _try_to_eval(self, epoch):
logger.save_extra_data(self.get_extra_data_to_save(epoch))
if self._can_evaluate():
self.evaluate(epoch)
params = self.get_epoch_snapshot(epoch)
logger.save_itr_params(epoch, params)
table_keys = logger.get_table_key_set()
if self._old_table_keys is not None:
assert table_keys == self._old_table_keys, (
"Table keys cannot change from iteration to iteration.")
self._old_table_keys = table_keys
logger.record_tabular(
"Number of train steps total",
self._n_train_steps_total,
)
logger.record_tabular(
"Number of env steps total",
self._n_env_steps_total,
)
logger.record_tabular(
"Number of rollouts total",
self._n_rollouts_total,
)
times_itrs = gt.get_times().stamps.itrs
train_time = times_itrs['train'][-1]
sample_time = times_itrs['sample'][-1]
eval_time = times_itrs['eval'][-1] if epoch > 0 else 0
epoch_time = train_time + sample_time + eval_time
total_time = gt.get_times().total
logger.record_tabular('Train Time (s)', train_time)
logger.record_tabular('(Previous) Eval Time (s)', eval_time)
logger.record_tabular('Sample Time (s)', sample_time)
logger.record_tabular('Epoch Time (s)', epoch_time)
logger.record_tabular('Total Train Time (s)', total_time)
logger.record_tabular("Epoch", epoch)
logger.dump_tabular(with_prefix=False, with_timestamp=False)
else:
logger.log("Skipping eval for now.")
def _can_evaluate(self):
"""
One annoying thing about the logger table is that the keys at each
iteration need to be the exact same. So unless you can compute
everything, skip evaluation.
A common example for why you might want to skip evaluation is that at
the beginning of training, you may not have enough data for a
validation and training set.
:return:
"""
# import pdb; pdb.set_trace()
return (
# len(self._exploration_paths) > 0
# and
self.replay_buffer.num_steps_can_sample(self.task_idx) >=
self.batch_size)
def _can_train(self):
return all([
self.replay_buffer.num_steps_can_sample(idx) >= self.batch_size
for idx in self.train_tasks
])
def _get_action_and_info(self, agent, observation):
"""
Get an action to take in the environment.
:param observation:
:return:
"""
agent.set_num_steps_total(self._n_env_steps_total)
return agent.get_action(observation, )
def _start_epoch(self, epoch):
self._epoch_start_time = time.time()
self._exploration_paths = []
self._do_train_time = 0
logger.push_prefix('Iteration #%d | ' % epoch)
def _end_epoch(self):
logger.log("Epoch Duration: {0}".format(time.time() -
self._epoch_start_time))
logger.log("Started Training: {0}".format(self._can_train()))
logger.pop_prefix()
def _start_new_rollout(self):
ret = self.env.reset()
if isinstance(ret, tuple):
ret = ret[0]
return ret
# not used
def _handle_path(self, path):
"""
Naive implementation: just loop through each transition.
:param path:
:return:
"""
for (ob, action, reward, next_ob, terminal, agent_info,
env_info) in zip(
path["observations"],
path["actions"],
path["rewards"],
path["next_observations"],
path["terminals"],
path["agent_infos"],
path["env_infos"],
):
self._handle_step(
ob.reshape(-1),
action,
reward,
next_ob.reshape(-1),
terminal,
agent_info=agent_info,
env_info=env_info,
)
self._handle_rollout_ending()
def _handle_step(
self,
task_idx,
observation,
action,
reward,
next_observation,
terminal,
agent_info,
env_info,
eval_task=False,
add_to_enc_buffer=True,
):
"""
Implement anything that needs to happen after every step
:return:
"""
self._current_path_builder.add_all(
task=task_idx,
observations=observation,
actions=action,
rewards=reward,
next_observations=next_observation,
terminals=terminal,
agent_infos=agent_info,
env_infos=env_info,
)
if eval_task:
self.eval_enc_replay_buffer.add_sample(
task=task_idx,
observation=observation,
action=action,
reward=reward,
terminal=terminal,
next_observation=next_observation,
agent_info=agent_info,
env_info=env_info,
)
else:
self.replay_buffer.add_sample(
task=task_idx,
observation=observation,
action=action,
reward=reward,
terminal=terminal,
next_observation=next_observation,
agent_info=agent_info,
env_info=env_info,
)
if add_to_enc_buffer:
self.enc_replay_buffer.add_sample(
task=task_idx,
observation=observation,
action=action,
reward=reward,
terminal=terminal,
next_observation=next_observation,
agent_info=agent_info,
env_info=env_info,
)
def _handle_rollout_ending(self, eval_task=False):
"""
Implement anything that needs to happen after every rollout.
"""
if eval_task:
self.eval_enc_replay_buffer.terminate_episode(self.task_idx)
else:
self.replay_buffer.terminate_episode(self.task_idx)
self.enc_replay_buffer.terminate_episode(self.task_idx)
self._n_rollouts_total += 1
if len(self._current_path_builder) > 0: # and False:
# self._exploration_paths.append(
# self._current_path_builder.get_all_stacked()
# )
self._current_path_builder = PathBuilder()
def get_epoch_snapshot(self, epoch):
data_to_save = dict(
epoch=epoch,
exploration_policy=self.exploration_policy,
)
if self.save_environment:
data_to_save['env'] = self.training_env
return data_to_save
def get_extra_data_to_save(self, epoch):
"""
Save things that shouldn't be saved every snapshot but rather
overwritten every time.
:param epoch:
:return:
"""
if self.render:
self.training_env.render(close=True)
data_to_save = dict(epoch=epoch, )
if self.save_environment:
data_to_save['env'] = self.training_env
if self.save_replay_buffer:
data_to_save['replay_buffer'] = self.replay_buffer
if self.save_algorithm:
data_to_save['algorithm'] = self
return data_to_save
@abc.abstractmethod
def training_mode(self, mode):
"""
Set training mode to `mode`.
:param mode: If True, training will happen (e.g. set the dropout
probabilities to not all ones).
"""
pass
@abc.abstractmethod
def evaluate(self, epoch):
"""
Evaluate the policy, e.g. save/print progress.
:param epoch:
:return:
"""
pass
@abc.abstractmethod
def _do_training(self):
"""
Perform some update, e.g. perform one gradient step.
:return:
"""
pass
class MetaRLAlgorithm(metaclass=abc.ABCMeta):
def __init__(
self,
env,
agent,
train_tasks,
eval_tasks,
meta_batch=64,
num_iterations=100,
num_train_steps_per_itr=1000,
num_initial_steps=100,
num_tasks_sample=100,
num_steps_prior=100,
num_steps_posterior=100,
num_extra_rl_steps_posterior=100,
num_evals=10,
num_steps_per_eval=1000,
batch_size=1024,
embedding_batch_size=1024,
embedding_mini_batch_size=1024,
max_path_length=1000,
discount=0.99,
replay_buffer_size=1000000,
reward_scale=1,
num_exp_traj_eval=1,
update_post_train=1,
eval_deterministic=True,
render=False,
save_replay_buffer=False,
save_algorithm=False,
save_environment=False,
render_eval_paths=False,
dump_eval_paths=False,
plotter=None,
):
"""
:param env: training env
:param agent: agent that is conditioned on a latent variable z that rl_algorithm is responsible for feeding in
:param train_tasks: list of tasks used for training
:param eval_tasks: list of tasks used for eval
see default experiment config file for descriptions of the rest of the arguments
"""
self.env = env
self.agent = agent
self.exploration_agent = agent # Can potentially use a different policy purely for exploration rather than also solving tasks, currently not being used
self.train_tasks = train_tasks
self.eval_tasks = eval_tasks
self.meta_batch = meta_batch
self.num_iterations = num_iterations
self.num_train_steps_per_itr = num_train_steps_per_itr
self.num_initial_steps = num_initial_steps
self.num_tasks_sample = num_tasks_sample
self.num_steps_prior = num_steps_prior
self.num_steps_posterior = num_steps_posterior
self.num_extra_rl_steps_posterior = num_extra_rl_steps_posterior
self.num_evals = num_evals
self.num_steps_per_eval = num_steps_per_eval
self.batch_size = batch_size
self.embedding_batch_size = embedding_batch_size
self.embedding_mini_batch_size = embedding_mini_batch_size
self.max_path_length = max_path_length
self.discount = discount
self.replay_buffer_size = replay_buffer_size
self.reward_scale = reward_scale
self.update_post_train = update_post_train
self.num_exp_traj_eval = num_exp_traj_eval
self.eval_deterministic = eval_deterministic
self.render = render
self.save_replay_buffer = save_replay_buffer
self.save_algorithm = save_algorithm
self.save_environment = save_environment
self.eval_statistics = None
self.render_eval_paths = render_eval_paths
self.dump_eval_paths = dump_eval_paths
self.plotter = plotter
# self.alpha = alpha
self.sampler = InPlacePathSampler(
env=env,
policy=agent,
max_path_length=self.max_path_length,
)
# separate replay buffers for
# - training RL update
# - training encoder update
self.replay_buffer = MultiTaskReplayBuffer(
self.replay_buffer_size,
env,
self.train_tasks,
)
self.enc_replay_buffer = MultiTaskReplayBuffer(
self.replay_buffer_size,
env,
self.train_tasks,
)
self._n_env_steps_total = 0
self._n_train_steps_total = 0
self._n_rollouts_total = 0
self._do_train_time = 0
self._epoch_start_time = None
self._algo_start_time = None
self._old_table_keys = None
self._current_path_builder = PathBuilder()
self._exploration_paths = []
def make_exploration_policy(self, policy):
return policy
def make_eval_policy(self, policy):
return policy
def sample_task(self, is_eval=False):
'''
sample task randomly
'''
if is_eval:
idx = np.random.randint(len(self.eval_tasks))
else:
idx = np.random.randint(len(self.train_tasks))
return idx
def train(self):
'''
meta-training loop
'''
self.pretrain()
params = self.get_epoch_snapshot(-1)
logger.save_itr_params(-1, params)
gt.reset()
gt.set_def_unique(False)
self._current_path_builder = PathBuilder()
# at each iteration, we first collect data from tasks, perform meta-updates, then try to evaluate
for it_ in gt.timed_for(
range(self.num_iterations),
save_itrs=True,
):
self._start_epoch(it_)
self.training_mode(True)
print("\nIteration:{}".format(it_+1))
if it_ == 0:# 算法第一步，初始化每个任务的buffer
print('\nCollecting initial pool of data for train and eval')
# temp for evaluating
for idx in self.train_tasks:#在训练开始之前，为每个任务采集2000条transition
self.task_idx = idx#更改当前任务idx
self.env.reset_task(idx)#重置任务
self.collect_data(self.num_initial_steps, 1, np.inf)#采集num_initial_steps条轨迹c并利用q(z|c)更新self.z
# print("task id:", self.task_idx, " env:", self.replay_buffer.env)
# print("buffer ", self.task_idx, ":", self.replay_buffer.task_buffers[self.task_idx].__dict__.items())
# Sample data from train tasks.
print("\nFinishing collecting initial pool of data")
print("\nSampling data from train tasks for Meta-training")
for i in range(self.num_tasks_sample):#对于所有的train_tasks，随机从中取5个，然后为每个任务的buffer采集num_steps_prior + num_extra_rl_steps_posterior条transition
print("\nSample data , round{}".format(i+1))#为每个任务的enc_buffer采集num_steps_prior条transition
idx = np.random.randint(len(self.train_tasks))#train_tasks里面随便选一个task
self.task_idx = idx
self.env.reset_task(idx)#task重置
self.enc_replay_buffer.task_buffers[idx].clear()#清除对应的enc_bufffer
# collect some trajectories with z ~ prior
if self.num_steps_prior > 0:
print("\ncollect some trajectories with z ~ prior")
self.collect_data(self.num_steps_prior, 1, np.inf)#利用z的先验采集num_steps_prior条transition
# collect some trajectories with z ~ posterior
if self.num_steps_posterior > 0:
print("\ncollect some trajectories with z ~ posterior")
self.collect_data(self.num_steps_posterior, 1, self.update_post_train)#利用后验的z收集轨迹
# even if encoder is trained only on samples from the prior, the policy needs to learn to handle z ~ posterior
if self.num_extra_rl_steps_posterior > 0:
print("\ncollect some trajectories for policy update only")
self.collect_data(self.num_extra_rl_steps_posterior, 1, self.update_post_train, add_to_enc_buffer=False)#利用后验的z收集num_extra_rl_steps_posterior条轨迹，仅用于策略
print("\nFinishing sample data from train tasks")
# Sample train tasks and compute gradient updates on parameters.
print("\nStrating Meta-training ， Episode {}".format(it_))
for train_step in range(self.num_train_steps_per_itr):#每轮迭代计算num_train_steps_per_itr次梯度 500x2000=1000000
indices = np.random.choice(self.train_tasks, self.meta_batch)#train_tasks中随机取meta_batch个task , sample RL batch b~B
if ((train_step + 1) % 500 == 0):
print("\nTraining step {}".format(train_step + 1))
print("Indices: {}".format(indices))
print("alpha:{}".format(self.alpha))
self._do_training(indices)#梯度下降
self._n_train_steps_total += 1
gt.stamp('train')
self.training_mode(False)
# eval
self._try_to_eval(it_)
gt.stamp('eval')
self._end_epoch()
def pretrain(self):
"""
Do anything before the main training phase.
"""
pass
def collect_data(self, num_samples, resample_z_rate, update_posterior_rate, add_to_enc_buffer=True):#在当前环境下，用当前self.agent.policy采样num_samples条轨迹
'''
get trajectories from current env in batch mode with given policy
collect complete trajectories until the number of collected transitions >= num_samples
:param agent: policy to rollout
:param num_samples: total number of transitions to sample 总共采集多少条轨迹
:param resample_z_rate: how often to resample latent context z (in units of trajectories)，每采集多少条轨迹，利用q(z|c)前向传播采样一次z
:param update_posterior_rate: how often to update q(z | c) from which z is sampled (in units of trajectories)，每多少条轨迹更新一次推断网络q(z|c)
:param add_to_enc_buffer: whether to add collected data to encoder replay buffer
'''
# start from the prior
self.agent.clear_z()
num_transitions = 0
while num_transitions < num_samples:#paths, n_steps_total返回轨迹与总步数
paths, n_samples = self.sampler.obtain_samples(max_samples=num_samples - num_transitions,#最大总步数
max_trajs=update_posterior_rate,#最大轨迹数量
accum_context=False,
resample=resample_z_rate)#resample_z_rate：根据c采样z的频率
num_transitions += n_samples#步数总数+=采样步数
self.replay_buffer.add_paths(self.task_idx, paths)#将该task下采集的轨迹加入经验池
print("\n buffer",self.task_idx, "size:", self.replay_buffer.task_buffers[self.task_idx].size())
# time.sleep(1)
# print("task id:", self.task_idx)
# print("buffer ", self.task_idx, ":", self.replay_buffer.task_buffers[self.task_idx])
# print("buffer ", self.task_idx, ":", self.replay_buffer.task_buffers[self.task_idx].__dict__.items())
# print("buffer ", self.task_idx, ":", self.replay_buffer.task_buffers[self.task_idx])
if add_to_enc_buffer:#是否加入encoder的buffer
self.enc_replay_buffer.add_paths(self.task_idx, paths)
# print("enc_buffer ", self.task_idx, ":", self.enc_replay_buffer.task_buffers[self.task_idx].__dict__.items())
# print("enc_buffer ", self.task_idx, ":",self.enc_replay_buffer.task_buffers[self.task_idx])
print("enc_buffer",self.task_idx, "size:", self.enc_replay_buffer.task_buffers[self.task_idx].size())
# time.sleep(1)
if update_posterior_rate != np.inf:#利用context更新后验z
# context = self.prepare_context(self.task_idx)
context = self.prepare_context(self.task_idx)
self.agent.infer_posterior(context)
self._n_env_steps_total += num_transitions
gt.stamp('sample')
# print("buffer ", self.task_idx, ":", self.replay_buffer.task_buffers[self.task_idx])
# print("enc_buffer ", self.task_idx, ":", self.enc_replay_buffer.task_buffers[self.task_idx])
def _try_to_eval(self, epoch):
logger.save_extra_data(self.get_extra_data_to_save(epoch))
if self._can_evaluate():
self.evaluate(epoch)
params = self.get_epoch_snapshot(epoch)
logger.save_itr_params(epoch, params)
table_keys = logger.get_table_key_set()
if self._old_table_keys is not None:
assert table_keys == self._old_table_keys, (
"Table keys cannot change from iteration to iteration."
)
self._old_table_keys = table_keys
logger.record_tabular(
"Number of train steps total",
self._n_train_steps_total,
)
logger.record_tabular(
"Number of env steps total",
self._n_env_steps_total,
)
logger.record_tabular(
"Number of rollouts total",
self._n_rollouts_total,
)
times_itrs = gt.get_times().stamps.itrs
train_time = times_itrs['train'][-1]
sample_time = times_itrs['sample'][-1]
eval_time = times_itrs['eval'][-1] if epoch > 0 else 0
epoch_time = train_time + sample_time + eval_time
total_time = gt.get_times().total
logger.record_tabular('Train Time (s)', train_time)
logger.record_tabular('(Previous) Eval Time (s)', eval_time)
logger.record_tabular('Sample Time (s)', sample_time)
logger.record_tabular('Epoch Time (s)', epoch_time)
logger.record_tabular('Total Train Time (s)', total_time)
logger.record_tabular("Epoch", epoch)
logger.dump_tabular(with_prefix=False, with_timestamp=False)
else:
logger.log("Skipping eval for now.")
def _can_evaluate(self):
"""
One annoying thing about the logger table is that the keys at each
iteration need to be the exact same. So unless you can compute
everything, skip evaluation.
A common example for why you might want to skip evaluation is that at
the beginning of training, you may not have enough data for a
validation and training set.
:return:
"""
# eval collects its own context, so can eval any time
return True
def _can_train(self):
return all([self.replay_buffer.num_steps_can_sample(idx) >= self.batch_size for idx in self.train_tasks])
def _get_action_and_info(self, agent, observation):
"""
Get an action to take in the environment.
:param observation:
:return:
"""
agent.set_num_steps_total(self._n_env_steps_total)
return agent.get_action(observation,)
def _start_epoch(self, epoch):
self._epoch_start_time = time.time()
self._exploration_paths = []
self._do_train_time = 0
logger.push_prefix('Iteration #%d | ' % epoch)
def _end_epoch(self):
logger.log("Epoch Duration: {0}".format(
time.time() - self._epoch_start_time
))
logger.log("Started Training: {0}".format(self._can_train()))
logger.pop_prefix()
##### Snapshotting utils #####
def get_epoch_snapshot(self, epoch):
data_to_save = dict(
epoch=epoch,
exploration_policy=self.exploration_policy,
)
if self.save_environment:
data_to_save['env'] = self.training_env
return data_to_save
def get_extra_data_to_save(self, epoch):
"""
Save things that shouldn't be saved every snapshot but rather
overwritten every time.
:param epoch:
:return:
"""
if self.render:
self.training_env.render(close=True)
data_to_save = dict(
epoch=epoch,
)
if self.save_environment:
data_to_save['env'] = self.training_env
if self.save_replay_buffer:
data_to_save['replay_buffer'] = self.replay_buffer
if self.save_algorithm:
data_to_save['algorithm'] = self
return data_to_save
def collect_paths(self, idx, epoch, run):
self.task_idx = idx
self.env.reset_task(idx)
self.agent.clear_z()
paths = []
num_transitions = 0
num_trajs = 0
while num_transitions < self.num_steps_per_eval:
path, num = self.sampler.obtain_samples(deterministic=self.eval_deterministic, max_samples=self.num_steps_per_eval - num_transitions, max_trajs=1, accum_context=True)
paths += path
num_transitions += num
num_trajs += 1
if num_trajs >= self.num_exp_traj_eval:
self.agent.infer_posterior(self.agent.context)#利用收集到的context,更新self.z
if self.sparse_rewards:
for p in paths:
sparse_rewards = np.stack(e['sparse_reward'] for e in p['env_infos']).reshape(-1, 1)
p['rewards'] = sparse_rewards
goal = self.env._goal
for path in paths:
path['goal'] = goal # goal
# save the paths for visualization, only useful for point mass
if self.dump_eval_paths:
logger.save_extra_data(paths, path='eval_trajectories/task{}-epoch{}-run{}'.format(idx, epoch, run))
return paths
def _do_eval(self, indices, epoch):
final_returns = []
online_returns = []
for idx in indices:
all_rets = []
for r in range(self.num_evals):
paths = self.collect_paths(idx, epoch, r)
all_rets.append([eval_util.get_average_returns([p]) for p in paths])
final_returns.append(np.mean([a[-1] for a in all_rets]))
# record online returns for the first n trajectories
n = min([len(a) for a in all_rets])
all_rets = [a[:n] for a in all_rets]
all_rets = np.mean(np.stack(all_rets), axis=0) # avg return per nth rollout
online_returns.append(all_rets)
n = min([len(t) for t in online_returns])
online_returns = [t[:n] for t in online_returns]
return final_returns, online_returns
def evaluate(self, epoch):
if self.eval_statistics is None:
self.eval_statistics = OrderedDict()
### sample trajectories from prior for debugging / visualization
if self.dump_eval_paths:
# 100 arbitrarily chosen for visualizations of point_robot trajectories
# just want stochasticity of z, not the policy
self.agent.clear_z()
prior_paths, _ = self.sampler.obtain_samples(deterministic=self.eval_deterministic, max_samples=self.max_path_length * 20,
accum_context=False,
resample=1)
logger.save_extra_data(prior_paths, path='eval_trajectories/prior-epoch{}'.format(epoch))
### train tasks
# eval on a subset of train tasks for speed
indices = np.random.choice(self.train_tasks, len(self.eval_tasks))#indices是任务编号的集合，从train_tasks中选择eval_tasks个任务，组成indices
eval_util.dprint('evaluating on {} train tasks'.format(len(indices)))
print('\nevaluating on {} train tasks'.format(len(indices)))
### eval train tasks with posterior sampled from the training replay buffer
train_returns = []
for idx in indices:#对于所有需要评估的任务
self.task_idx = idx
self.env.reset_task(idx)
paths = []
for _ in range(self.num_steps_per_eval // self.max_path_length):#总共用多少条path进行评估 600/200
# context = self.prepare_context(idx)#c~Sc(B)
context = self.prepare_context(idx)
# print("context:",context)
self.agent.infer_posterior(context)#z~q(z|c)
p, _ = self.sampler.obtain_samples(deterministic=self.eval_deterministic, max_samples=self.max_path_length,
accum_context=False,
max_trajs=1,
resample=np.inf)
paths += p #收集200条paths
if self.sparse_rewards:
for p in paths:
sparse_rewards = np.stack(e['sparse_reward'] for e in p['env_infos']).reshape(-1, 1)
p['rewards'] = sparse_rewards
train_returns.append(eval_util.get_average_returns(paths))#200条path的轨迹平均
# print(" train_returns:{}".format(train_returns))
train_returns = np.mean(train_returns)#把轨迹获得的奖赏加起来取平均
# print(" train_returns:{}".format(train_returns))
# time.sleep(5)
### eval train tasks with on-policy data to match eval of test tasks
train_final_returns, train_online_returns = self._do_eval(indices, epoch)
print("train_final_returns:{}".format(train_final_returns))
# print("train_online_returns:{}".format(train_online_returns))
eval_util.dprint('train online returns')
eval_util.dprint(train_online_returns)
### test tasks
eval_util.dprint('evaluating on {} test tasks'.format(len(self.eval_tasks)))
print('\nevaluating on {} test tasks'.format(len(self.eval_tasks)))
test_final_returns, test_online_returns = self._do_eval(self.eval_tasks, epoch)
print("test_final_returns:{}".format(test_final_returns))
# print("test_online_returns:{}".format(test_online_returns))
eval_util.dprint('test online returns')
eval_util.dprint(test_online_returns)
# save the final posterior
self.agent.log_diagnostics(self.eval_statistics)
if hasattr(self.env, "log_diagnostics"):
#self.env.log_diagnostics(paths, prefix=None)
self.env.log_diagnostics(paths)
avg_train_return = np.mean(train_final_returns)
avg_test_return = np.mean(test_final_returns)
# print("\ntrain_returns:{}".format(train_returns))
print("\navg_train_return:{}".format(avg_train_return))
print("avg_test_return:{}".format(avg_test_return))
time.sleep(5)
avg_train_online_return = np.mean(np.stack(train_online_returns), axis=0)
avg_test_online_return = np.mean(np.stack(test_online_returns), axis=0)
self.eval_statistics['AverageTrainReturn_all_train_tasks'] = train_returns
self.eval_statistics['AverageReturn_all_train_tasks'] = avg_train_return
self.eval_statistics['AverageReturn_all_test_tasks'] = avg_test_return
logger.save_extra_data(avg_train_online_return, path='online-train-epoch{}'.format(epoch))
logger.save_extra_data(avg_test_online_return, path='online-test-epoch{}'.format(epoch))
for key, value in self.eval_statistics.items():
logger.record_tabular(key, value)
self.eval_statistics = None
if self.render_eval_paths:
self.env.render_paths(paths)
if self.plotter:
self.plotter.draw()
@abc.abstractmethod
def training_mode(self, mode):
"""
Set training mode to `mode`.
:param mode: If True, training will happen (e.g. set the dropout
probabilities to not all ones).
"""
pass
@abc.abstractmethod
def _do_training(self):
"""
Perform some update, e.g. perform one gradient step.
:return:
"""
pass