categorical_gru_policy.py 文件源码

def get_action(self, observation):
        if self.state_include_action:
            if self.prev_action is None:
                prev_action = np.zeros((self.action_space.flat_dim,))
            else:
                prev_action = self.action_space.flatten(self.prev_action)
            all_input = np.concatenate([
                self.observation_space.flatten(observation),
                prev_action
            ])
        else:
            all_input = self.observation_space.flatten(observation)
            # should not be used
            prev_action = np.nan
        probs, hidden_vec = [x[0] for x in self.f_step_prob([all_input], [self.prev_hidden])]
        action = special.weighted_sample(probs, range(self.action_space.n))
        self.prev_action = action
        self.prev_hidden = hidden_vec
        agent_info = dict(prob=probs)
        if self.state_include_action:
            agent_info["prev_action"] = prev_action
        return action, agent_info