def choose_action(self, state):
state = torch.from_numpy(state).float().unsqueeze(0)
mean, logstd = self.policy(state.cuda())
dist = Independent(Normal(mean.squeeze(), torch.exp(logstd)), 1)
action = dist.sample()
log_prob = dist.log_prob(action)
return action.squeeze().cpu().numpy(), log_prob.item()
def choose_action(self, state):
state = torch.from_numpy(state).float().unsqueeze(0)
mean, logstd = self.policy(state)
dist = Independent(Normal(mean, torch.exp(logstd)), 1)
return dist.sample().squeeze().numpy()
def get_act(mean, std, amount=None):
dist = Independent(Normal(mean, std), reinterpreted_batch_ndims=1)
if amount is not None:
return dist.sample(amount)
return dist.sample()