discrete_action_trainer.py 文件源码

def get_q_values_all_actions(
        self, states: np.ndarray, use_target_network: Optional[bool] = True
    ) -> np.ndarray:
        """
        Takes in a set of states and runs the test Q Network on them.

        Creates Q(states, actions), a blob with shape (batch_size, action_dim).
        Q(states, actions)[i][j] is an approximation of Q*(states[i], action_j).
        Note that action_j takes on every possible action (of which there are
        self.action_dim_. Stores blob in self.output_blob and returns its value.

        :param states: Numpy array with shape (batch_size, state_dim). Each row
            contains a representation of a state.
        :param possible_next_actions: Numpy array with shape (batch_size, action_dim).
            possible_next_actions[i][j] = 1 iff the agent can take action j from
            state i.
        :use_target_network: Boolean that indicates whether or not to use this
            trainer's TargetNetwork to compute Q values.
        """
        if use_target_network:
            return self.target_network.target_values(states)
        return self.score(states)