def _get_samples(self, batch_inds: np.ndarray, env: Optional[VecNormalize] = None) -> RolloutBufferSamples:
data = (
self.observations[batch_inds],
self.actions[batch_inds],
self.values[batch_inds].flatten(),
self.log_probs[batch_inds].flatten(),
self.advantages[batch_inds].flatten(),
self.returns[batch_inds].flatten(),
)
if isinstance(self.observation_space, JsonGraph):
return RolloutBufferSamples(data[0],*tuple(map(self.to_torch, data[1:])))
else:
return RolloutBufferSamples(*tuple(map(self.to_torch, data)))
def format_trajectories(trajectories : List[TrajectoryBufferSamples]) -> RolloutBufferSamples:
observations = np.concatenate([t.observations for t in trajectories]).squeeze()
contexts = np.concatenate([np.broadcast_to(t.context, (t.observations.shape[0],) + t.context.shape) for t in trajectories])
observations = np.concatenate([observations, contexts], axis=-1).squeeze()
actions = np.concatenate([t.actions for t in trajectories]).squeeze()
returns = np.concatenate([t.returns for t in trajectories]).squeeze()
values = np.concatenate([t.values for t in trajectories]).squeeze()
log_probs = np.concatenate([t.log_probs for t in trajectories]).squeeze()
advantages = np.concatenate([t.advantages for t in trajectories]).squeeze()
try:
context_error = np.concatenate([t.context_error for t in trajectories]).squeeze()
except:
context_error = np.zeros(values.shape)
indices = np.arange(actions.shape[0])
np.random.shuffle(indices)
data = (
th.tensor(observations[indices]),
th.tensor(actions[indices]),
th.tensor(values[indices]),
th.tensor(log_probs[indices]),
th.tensor(advantages[indices]),
th.tensor(returns[indices]),
th.tensor(context_error[indices])
)
return RolloutBufferSamples(*tuple(map(th.tensor, data)))
def _get_samples(self, batch_inds: np.ndarray,
env: Optional[VecNormalize] = None) -> RolloutBufferSamples:
data = (self.observations[batch_inds],
self.actions[batch_inds],
self.values[batch_inds].flatten(),
self.log_probs[batch_inds].flatten(),
self.advantages[batch_inds].flatten(),
self.returns[batch_inds].flatten())
return RolloutBufferSamples(*tuple(map(self.to_torch, data)))
