678f40aa5244d57280310f9421b9ba0727241fbc,chainerrl/agents/ddpg.py,DDPG,act_and_train,#DDPG#Any#Any#,285

Before Change

        self.logger.debug("t:%s r:%s", self.t, reward)

        if self.async_update and self.update_future:
            self.update_future.result()
            self.update_future = None

        if self.clip_reward:
            reward = np.clip(reward, -1, 1)

        greedy_action = self.act(state)
        action = self.explorer.select_action(self.t, lambda: greedy_action)
        self.t += 1

        // Update the target network
        if self.t % self.target_update_frequency == 0:
            self.sync_target_network()

        if self.last_state is not None:
            assert self.last_action is not None
            // Add a transition to the replay buffer
            self.replay_buffer.append(
                state=self.last_state,
                action=self.last_action,
                reward=reward,
                next_state=state,
                next_action=action,
                is_state_terminal=False)

        self.last_state = state
        self.last_action = action

        self.update_if_necessary()

        return self.last_action

    def update_if_necessary(self):

After Change

                actor_loss += self.compute_actor_loss(batch)
            self.actor_optimizer.update(lambda: actor_loss / max_epi_len)

    def act_and_train(self, state, reward):

        self.logger.debug("t:%s r:%s", self.t, reward)

        if self.clip_reward:
            reward = np.clip(reward, -1, 1)

        greedy_action = self.act(state)
        action = self.explorer.select_action(self.t, lambda: greedy_action)
        self.t += 1

        // Update the target network
        if self.t % self.target_update_frequency == 0:
            self.sync_target_network()

        if self.last_state is not None:
            assert self.last_action is not None
            // Add a transition to the replay buffer
            self.replay_buffer.append(
                state=self.last_state,
                action=self.last_action,
                reward=reward,
                next_state=state,
                next_action=action,
                is_state_terminal=False)

        self.last_state = state
        self.last_action = action

        self.replay_updator.update_if_necessary(self.t)

        return self.last_action

    def act(self, state):

Instances