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Autonomous lane changing is a critical feature for advanced autonomousdriving systems, that involves several challenges such as uncertainty in otherdriver's behaviors and the trade-off between safety and agility. In this work,we develop a novel simulation environment that emulates these challenges andtrain a deep reinforcement learning agent that yields consistent performance ina variety of dynamic and uncertain traffic scenarios. Results show that theproposed data-driven approach performs significantly better in noisyenvironments compared to methods that rely solely on heuristics.

Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment