Monte-Carlo Tree Search in Continuous Action Spaces for Autonomous Racing : F1-tenth

Abstract: Autonomous cars involve problems with control and planning. In thispaper, we implement and evaluate an autonomous agent based ona Monte-Carlo Tree Search in continuous action space. To facilitatethe algorithm, we extend an existing simulation framework and usea GPU for faster calculations. We compare three action generatorsand two rewards functions. The results show that MCTS convergesto an effective driving agent in static environments. However, it onlysucceeds at driving slow speeds in real-time. We discuss the problemsthat arise in dynamic and static environments and look to future workin improving the simulation tool and the MCTS algorithm. See code, https://github.com/felrock/PyRacecarSimulator