def _act(self, obs):
# TODO implement non-sampling validation mode
obs = map2torch(self._obs_normalizer(obs), self._hp.device)
if len(obs.shape) == 1: # we need batched inputs for policy
policy_output = self._remove_batch(self.policy(obs[None]))
if 'dist' in policy_output:
del policy_output['dist']
return map2np(policy_output)
return map2np(self.policy(obs))
def run(self, inputs, use_learned_prior=True):
"""Policy interface for model. Runs decoder if action plan is empty, otherwise returns next action from action plan.
:arg inputs: dict with 'states', 'actions', 'images' keys from environment
:arg use_learned_prior: if True, uses learned prior otherwise samples latent from uniform prior
"""
if not self._action_plan:
inputs = map2torch(inputs, device=self.device)
# sample latent variable from prior
z = self.compute_learned_prior(self._learned_prior_input(inputs), first_only=True).sample() \
if use_learned_prior else Gaussian(torch.zeros((1, self._hp.nz_vae*2), device=self.device)).sample()
# decode into action plan
z = z.repeat(
self._hp.batch_size, 1
) # this is a HACK flat LSTM decoder can only take batch_size inputs
input_obs = self._learned_prior_input(inputs).repeat(
self._hp.batch_size, 1)
actions = self.decode(z,
cond_inputs=input_obs,
steps=self._hp.n_rollout_steps)[0]
self._action_plan = deque(split_along_axis(map2np(actions),
axis=0))
return AttrDict(action=self._action_plan.popleft()[None])
def _split_obs(self, obs):
unflattened_obs = map2np(
self._policy.unflatten_obs(
map2torch(obs[:, :-self._policy.latent_dim],
device=self.device)))
return AttrDict(
cond_input=unflattened_obs.prior_obs,
z=obs[:, -self._policy.latent_dim:],
)
def _act(self, obs):
assert len(obs.shape) == 2 and obs.shape[
0] == 1 # assume single-observation batches with leading 1-dim
if not self.action_plan:
# generate action plan if the current one is empty
split_obs = self._split_obs(obs)
with no_batchnorm_update(
self._policy
) if obs.shape[0] == 1 else contextlib.suppress():
actions = self._policy.decode(
map2torch(split_obs.z, self._hp.device),
map2torch(split_obs.cond_input, self._hp.device),
self._policy.n_rollout_steps)
self.action_plan = deque(split_along_axis(map2np(actions), axis=1))
return AttrDict(action=self.action_plan.popleft())
def _act_rand(self, obs):
policy_output = self.policy.sample_rand(
map2torch(obs, self.policy.device))
if 'dist' in policy_output:
del policy_output['dist']
return map2np(policy_output)
