def _train_value_function(self, paths):
"""Train the value function.
Args:
paths (list[dict]): A list of collected paths.
Returns:
torch.Tensor: Calculated mean scalar value of value function loss
(float).
"""
# MAML resets a value function to its initial state before training.
self._value_function.load_state_dict(self._initial_vf_state)
obs = np.concatenate([path['observations'] for path in paths], axis=0)
returns = np.concatenate([path['returns'] for path in paths])
obs = np_to_torch(obs)
returns = np_to_torch(returns.astype(np.float32))
vf_loss = self._value_function.compute_loss(obs, returns)
# pylint: disable=protected-access
zero_optim_grads(self._inner_algo._vf_optimizer._optimizer)
vf_loss.backward()
# pylint: disable=protected-access
self._inner_algo._vf_optimizer.step()
return vf_loss
def adapt_policy(self, exploration_policy, exploration_episodes):
"""Produce a policy adapted for a task.
Args:
exploration_policy (Policy): A policy which was returned from
get_exploration_policy(), and which generated
exploration_episodes by interacting with an environment.
The caller may not use this object after passing it into this
method.
exploration_episodes (EpisodeBatch): Episodes to which to adapt,
generated by exploration_policy exploring the
environment.
Returns:
Policy: A policy adapted to the task represented by the
exploration_episodes.
"""
total_steps = sum(exploration_episodes.lengths)
o = exploration_episodes.observations
a = exploration_episodes.actions
r = exploration_episodes.rewards.reshape(total_steps, 1)
ctxt = np.hstack((o, a, r)).reshape(1, total_steps, -1)
context = np_to_torch(ctxt)
self._policy.infer_posterior(context)
return self._policy
def get_action(self, observation):
r"""Get a single action given an observation.
Args:
observation (np.ndarray): Observation from the environment.
Shape is :math:`env_spec.observation_space`.
Returns:
tuple:
* np.ndarray: Predicted action. Shape is
:math:`env_spec.action_space`.
* dict:
* np.ndarray[float]: Mean of the distribution
* np.ndarray[float]: Standard deviation of logarithmic
values of the distribution.
"""
if not isinstance(observation, np.ndarray) and not isinstance(
observation, torch.Tensor):
observation = self._env_spec.observation_space.flatten(observation)
elif isinstance(observation,
np.ndarray) and len(observation.shape) > 1:
observation = self._env_spec.observation_space.flatten(observation)
elif isinstance(observation,
torch.Tensor) and len(observation.shape) > 1:
observation = torch.flatten(observation)
with torch.no_grad():
if isinstance(observation, np.ndarray):
observation = np_to_torch(observation)
if not isinstance(observation, torch.Tensor):
observation = list_to_tensor(observation)
observation = observation.unsqueeze(0)
action, agent_infos = self.get_actions(observation)
return action[0], {k: v[0] for k, v in agent_infos.items()}
def _sample_data(self, indices):
"""Sample batch of training data from a list of tasks.
Args:
indices (list): List of task indices to sample from.
Returns:
torch.Tensor: Obervations, with shape :math:`(X, N, O^*)` where X
is the number of tasks. N is batch size.
torch.Tensor: Actions, with shape :math:`(X, N, A^*)`.
torch.Tensor: Rewards, with shape :math:`(X, N, 1)`.
torch.Tensor: Next obervations, with shape :math:`(X, N, O^*)`.
torch.Tensor: Dones, with shape :math:`(X, N, 1)`.
"""
# transitions sampled randomly from replay buffer
initialized = False
for idx in indices:
batch = self._replay_buffers[idx].sample_transitions(
self._batch_size)
if not initialized:
o = batch['observations'][np.newaxis]
a = batch['actions'][np.newaxis]
r = batch['rewards'][np.newaxis]
no = batch['next_observations'][np.newaxis]
d = batch['dones'][np.newaxis]
initialized = True
else:
o = np.vstack((o, batch['observations'][np.newaxis]))
a = np.vstack((a, batch['actions'][np.newaxis]))
r = np.vstack((r, batch['rewards'][np.newaxis]))
no = np.vstack((no, batch['next_observations'][np.newaxis]))
d = np.vstack((d, batch['dones'][np.newaxis]))
o = np_to_torch(o)
a = np_to_torch(a)
r = np_to_torch(r)
no = np_to_torch(no)
d = np_to_torch(d)
return o, a, r, no, d
def get_actions(self, observations):
r"""Get actions given observations.
Args:
observations (np.ndarray): Observations from the environment.
Shape is :math:`batch_dim \bullet env_spec.observation_space`.
Returns:
tuple:
* np.ndarray: Predicted actions.
:math:`batch_dim \bullet env_spec.action_space`.
* dict:
* np.ndarray[float]: Mean of the distribution.
* np.ndarray[float]: Standard deviation of logarithmic
values of the distribution.
"""
if not isinstance(observations[0], np.ndarray) and not isinstance(
observations[0], torch.Tensor):
observations = self._env_spec.observation_space.flatten_n(
observations)
# frequently users like to pass lists of torch tensors or lists of
# numpy arrays. This handles those conversions.
if isinstance(observations, list):
if isinstance(observations[0], np.ndarray):
observations = np.stack(observations)
elif isinstance(observations[0], torch.Tensor):
observations = torch.stack(observations)
if isinstance(observations[0],
np.ndarray) and len(observations[0].shape) > 1:
observations = self._env_spec.observation_space.flatten_n(
observations)
elif isinstance(observations[0],
torch.Tensor) and len(observations[0].shape) > 1:
observations = torch.flatten(observations, start_dim=1)
with torch.no_grad():
if isinstance(observations, np.ndarray):
observations = np_to_torch(observations)
if not isinstance(observations, torch.Tensor):
observations = list_to_tensor(observations)
if isinstance(self._env_spec.observation_space, akro.Image):
observations /= 255.0 # scale image
dist, info = self.forward(observations)
return dist.sample().cpu().numpy(), {
k: v.detach().cpu().numpy()
for (k, v) in info.items()
}
def _sample_context(self, indices):
"""Sample batch of context from a list of tasks.
Args:
indices (list): List of task indices to sample from.
Returns:
torch.Tensor: Context data, with shape :math:`(X, N, C)`. X is the
number of tasks. N is batch size. C is the combined size of
observation, action, reward, and next observation if next
observation is used in context. Otherwise, C is the combined
size of observation, action, and reward.
"""
# make method work given a single task index
if not hasattr(indices, '__iter__'):
indices = [indices]
initialized = False
for idx in indices:
batch = self._context_replay_buffers[idx].sample_transitions(
self._embedding_batch_size)
o = batch['observations']
a = batch['actions']
r = batch['rewards']
context = np.hstack((np.hstack((o, a)), r))
if self._use_next_obs_in_context:
context = np.hstack((context, batch['next_observations']))
if not initialized:
final_context = context[np.newaxis]
initialized = True
else:
final_context = np.vstack((final_context, context[np.newaxis]))
final_context = np_to_torch(final_context)
if len(indices) == 1:
final_context = final_context.unsqueeze(0)
return final_context
def _train_once(self, itr, eps):
"""Train the algorithm once.
Args:
itr (int): Iteration number.
eps (EpisodeBatch): A batch of collected paths.
Returns:
numpy.float64: Calculated mean value of undiscounted returns.
"""
obs = np_to_torch(eps.padded_observations)
rewards = np_to_torch(eps.padded_rewards)
returns = np_to_torch(
np.stack([
discount_cumsum(reward, self.discount)
for reward in eps.padded_rewards
]))
valids = eps.lengths
with torch.no_grad():
baselines = self._value_function(obs)
if self._maximum_entropy:
policy_entropies = self._compute_policy_entropy(obs)
rewards += self._policy_ent_coeff * policy_entropies
obs_flat = np_to_torch(eps.observations)
actions_flat = np_to_torch(eps.actions)
rewards_flat = np_to_torch(eps.rewards)
returns_flat = torch.cat(filter_valids(returns, valids))
advs_flat = self._compute_advantage(rewards, valids, baselines)
with torch.no_grad():
policy_loss_before = self._compute_loss_with_adv(
obs_flat, actions_flat, rewards_flat, advs_flat)
vf_loss_before = self._value_function.compute_loss(
obs_flat, returns_flat)
kl_before = self._compute_kl_constraint(obs)
self._train(obs_flat, actions_flat, rewards_flat, returns_flat,
advs_flat)
with torch.no_grad():
policy_loss_after = self._compute_loss_with_adv(
obs_flat, actions_flat, rewards_flat, advs_flat)
vf_loss_after = self._value_function.compute_loss(
obs_flat, returns_flat)
kl_after = self._compute_kl_constraint(obs)
policy_entropy = self._compute_policy_entropy(obs)
with tabular.prefix(self.policy.name):
tabular.record('/LossBefore', policy_loss_before.item())
tabular.record('/LossAfter', policy_loss_after.item())
tabular.record('/dLoss',
(policy_loss_before - policy_loss_after).item())
tabular.record('/KLBefore', kl_before.item())
tabular.record('/KL', kl_after.item())
tabular.record('/Entropy', policy_entropy.mean().item())
with tabular.prefix(self._value_function.name):
tabular.record('/LossBefore', vf_loss_before.item())
tabular.record('/LossAfter', vf_loss_after.item())
tabular.record('/dLoss',
vf_loss_before.item() - vf_loss_after.item())
self._old_policy.load_state_dict(self.policy.state_dict())
undiscounted_returns = log_performance(itr,
eps,
discount=self._discount)
return np.mean(undiscounted_returns)