  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.
