Beyond Trial and Error: Lane Keeping with Monte Carlo Tree Search-Driven Optimization of Reinforcement Learning
Bálint Kővári,
Bálint Pelenczei,
István Gellért Knáb
et al.
Abstract:In recent years, Reinforcement Learning (RL) has excelled in the realm of autonomous vehicle control, which is distinguished by the absence of limitations, such as specific training data or the necessity for explicit mathematical model identification. Particularly in the context of lane keeping, a diverse set of rewarding strategies yields a spectrum of realizable policies. Nevertheless, the challenge lies in discerning the optimal behavior that maximizes performance. Traditional approaches entail exhaustive t… Show more
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