Highway Lane Change Decision-Making via Attention-Based Deep Reinforcement Learning

Deep reinforcement learning (DRL), combining the perception capability of deep learning (DL) and the decision-making capability of reinforcement learning (RL) [1], has been widely investigated for autonomous driving decision-making tasks. In this letter, we would like to discuss the impact of different types of state input on the performance of DRL-based lane change decision-making.