Towards Comprehensive Maneuver Decisions for Lane Change Using Reinforcement Learning
NeurIPS workshop on MLITS, 2018.
In this paper, we consider the problem of autonomous lane changing for self driving vehicles in a multi-lane, multi-agent setting. We use reinforcement learning solely to obtain a high-level policy for decision making, while the lower level action is executed by a pre-defined controller. To obtain a comprehensive model adaptive to as wide...More
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