关键词: 深度强化学习；自动驾驶；后门攻击；端到端

Abstract: In recent years, backdoor attacks on deep reinforcement learning have attracted widespread attention. Among the studies that have been published so far, no universal and interpretable backdoor attack framework has been proposed. To solve this problem, the article proposes a novel backdoor attack technique, which is called edge attack. A backdoor agent is trained through two stages. Especially during the training process, when the environmental observation vector meets the set conditions, triggers are added to the environment and a specially designed reward function is used. The article evaluates the effectiveness of the proposed method on four of the most advanced deep reinforcement learning algorithms. The experimental results show that when the backdoor is hidden, the success rate of the backdoor agent in handling the automatic parking task reaches 93%. However, once the backdoor is activated, the attack success rate reaches 94.3%, while the success rate of the automatic parking task decreases to 5.7%.

Key words: deep reinforcement learning; autonomous driving; backdoor attack; end-to-end