def act(self, state):
if self.body.env.clock.frame < self.training_start_step:
return policy_util.random(state, self, self.body).cpu().squeeze().numpy()
else:
action = self.action_policy(state, self, self.body)
if not self.body.is_discrete:
action = self.scale_action(torch.tanh(action)) # continuous action bound
return action.cpu().squeeze().numpy()
def act(self, state):
if self.body.env.clock.frame < self.training_start_step:
return policy_util.random(state, self,
self.body).cpu().squeeze().numpy()
else:
action = self.action_policy(state, self, self.body)
if self.body.is_discrete:
# discrete output is RelaxedOneHotCategorical, need to sample to int
action = torch.distributions.Categorical(probs=action).sample()
else:
action = torch.tanh(action) # continuous action bound
return action.cpu().squeeze().numpy()