Deep reinforcement learning is actively used for training autonomous driving agents in a vision-based urban simulated environment. Due to the large availability of various reinforcement learning algorithms, we are still unsure of which one works better while training autonomous cars in single-agent as well as multi-agent driving environments. A comparison of deep reinforcement learning in vision-based autonomous driving will open up the possibilities for training better autonomous car policies. Also, autonomous cars trained on deep reinforcement learning-based algorithms are known for being vulnerable to adversarial attacks, and we have less information on which algorithms would act as a good adversarial agent. In this work, we provide a systematic evaluation and comparative analysis of 6 deep reinforcement learning algorithms for autonomous and adversarial driving in four-way intersection scenario. Specifically, we first train autonomous cars using state-of-the-art deep reinforcement learning algorithms. Second, we test driving capabilities of the trained autonomous policies in single-agent as well as multi-agent scenarios. Lastly, we use the same deep reinforcement learning algorithms to train adversarial driving agents, in order to test the driving performance of autonomous cars and look for possible collision and offroad driving scenarios. We perform experiments by using vision-only high fidelity urban driving simulated environments.
翻译:深层强化学习被积极用于培训基于愿景的城市模拟环境中的自主驾驶器。由于有大量各种强化学习算法,我们仍不确定在单一试剂和多试剂驾驶环境中培训自主驾驶器的深度强化学习方法的比较将为培训更好的自主驾驶器政策开辟可能性。此外,受过深强化学习算法培训的自主驾驶器因易受对抗性攻击而为人所知,我们较少了解哪些算法会成为良好的对抗性代理器。在这项工作中,我们对四路交叉情况下自主驾驶和对抗性驾驶的6种深层强化学习算法进行系统评估和比较分析。具体地说,我们首先使用最先进的深层强化算法培训自主驾驶器。第二,我们测试经培训的单试剂自动驾驶器的驾驶能力以及多试想式。最后,我们使用同样的深强化学习算法来培训对抗性驾驶器,以测试自主驾驶器的性能,并寻找可能的碰撞和越轨机动驾驶场。我们用高诚实的模拟城市驾驶环境进行实验。