In this paper, we consider the problem of autonomous lane changing for self driving vehicles in a multi-lane, multi-agent setting. We present a framework that demonstrates a more structured and data efficient alternative to end-to-end complete policy learning on problems where the high-level policy is hard to formulate using traditional optimization or rule based methods but well designed low-level controllers are available. Our framework uses deep reinforcement learning solely to obtain a high-level policy for tactical decision making, while still maintaining a tight integration with the low-level controller, thus getting the best of both worlds. We accomplish this with Q-masking, a technique with which we are able to incorporate prior knowledge, constraints, and information from a low-level controller, directly in to the learning process thereby simplifying the reward function and making learning faster and data efficient. We provide preliminary results in a simulator and show our approach to be more efficient than a greedy baseline, and more successful and safer than human driving.
A framework that provides a policy for autonomous lane changing by learning to make high-level tactical decisions with deep reinforcement learning, and maintaining a tight integration with a low-level controller to take low-level actions. Considers the problem of autonomous lane changing for self-driving cars in multi-lane multi-agent slot car setting, proposes a new learning strategy Q-masking - coupling a defined low level controller with a high level tactical decision making policy. This paper proposes a deep Q-learning approach to the problem of lane change using "Q-masking," which reduces the action space according to contraints or prior knowledge. Authors propose a method which uses a Q-learning-based high-level policy that is combined with a contextual mask derived from safety-contraints and low-level controllers, which disable certain actions from being selectable at certain states. 