Urban autonomous driving decision making is challenging due to complex road
geometry and multi-agent interactions. Current decision making methods are
mostly manually designing the driving policy, which might result in sub-optimal
solutions and is expensive to develop, generalize and maintain at scale. On the
other hand, with reinforcement learning (RL), a policy can be learned and
improved automatically without any manual designs. However, current RL methods
generally do not work well on complex urban scenarios. In this paper, we
propose a framework to enable model-free deep reinforcement learning in
challenging urban autonomous driving scenarios. We design a specific input
representation and use visual encoding to capture the low-dimensional latent
states. Several state-of-the-art model-free deep RL algorithms are implemented
into our framework, with several tricks to improve their performance. We
evaluate our method in a challenging roundabout task with dense surrounding
vehicles in a high-definition driving simulator. The result shows that our
method can solve the task well and is significantly better than the baseline.

本文提出了一个在复杂城市自主驾驶场景下使用无模型深度强化学习的框架，并在高清晰度驾驶模拟器中进行了验证。结果表明，与基线相比，我们的方法可以很好地解决任务，且表现显著优异。