def get_action(self, belief, state, det=False, scale=None):
action_mean, action_std = self.forward(belief, state)
if scale:
#exploration distribution
dist = Normal(action_mean,
action_std + action_std.detach() * (1 - scale))
dist = TransformedDistribution(dist, TanhBijector())
dist = torch.distributions.Independent(dist, 1)
dist = SampleDist(dist)
action = dist.mode() if det else dist.rsample()
proposal_loglike = dist.log_prob(action).detach()
#true distribution
dist = Normal(action_mean, action_std)
dist = TransformedDistribution(dist, TanhBijector())
dist = torch.distributions.Independent(dist, 1)
dist = SampleDist(dist)
policy_loglike = dist.log_prob(action)
return action, policy_loglike, proposal_loglike
else:
dist = Normal(action_mean, action_std)
dist = TransformedDistribution(dist, TanhBijector())
dist = torch.distributions.Independent(dist, 1)
dist = SampleDist(dist)
action = dist.mode() if det else dist.rsample()
return action
def get_action(self, belief, state, det=False):
action_mean, action_std = self.forward(belief, state)
dist = Normal(action_mean, action_std)
dist = TransformedDistribution(dist, TanhBijector())
dist = torch.distributions.Independent(dist, 1)
dist = SampleDist(dist)
if det: return dist.mode()
else: return dist.rsample()
