def sample_rand(self, obs):
with torch.no_grad():
with no_batchnorm_update(self.prior_net):
prior_dist = self.prior_net.compute_learned_prior(obs, first_only=True).detach()
action = prior_dist.sample()
action, log_prob = self._tanh_squash_output(action, 0) # ignore log_prob output
return AttrDict(action=action, log_prob=log_prob)
def _compute_prior_divergence(self, policy_output, obs):
with no_batchnorm_update(self.prior_net):
prior_dist = self.prior_net.compute_learned_prior(
obs, first_only=True).detach()
if self._hp.analytic_KL:
return self._analytic_divergence(policy_output,
prior_dist), prior_dist
return self._mc_divergence(policy_output, prior_dist), prior_dist
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 forward(self, obs):
with no_batchnorm_update(
self): # BN updates harm the initialized policy
return super().forward(obs)
def forward(self, obs):
with no_batchnorm_update(self):
return LearnedPriorAugmentedPolicy.forward(self, obs)
