Model-based versus Model-free Deep Reinforcement Learning for Autonomous Racing Cars

• URL: http://arxiv.org/abs/2103.04909v1
• Date: Mon, 8 Mar 2021 17:15:23 GMT
• Title: Model-based versus Model-free Deep Reinforcement Learning for Autonomous Racing Cars
• Authors: Axel Brunnbauer, Luigi Berducci, Andreas Brandst\"atter, Mathias Lechner, Ramin Hasani, Daniela Rus, Radu Grosu
• Abstract summary: This paper investigates how model-based deep reinforcement learning agents generalize to real-world autonomous-vehicle control-tasks. We show that model-based agents capable of learning in imagination, substantially outperform model-free agents with respect to performance, sample efficiency, successful task completion, and generalization.
• Score: 46.64253693115981
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the rich theoretical foundation of model-based deep reinforcement learning (RL) agents, their effectiveness in real-world robotics-applications is less studied and understood. In this paper, we, therefore, investigate how such agents generalize to real-world autonomous-vehicle control-tasks, where advanced model-free deep RL algorithms fail. In particular, we set up a series of time-lap tasks for an F1TENTH racing robot, equipped with high-dimensional LiDAR sensors, on a set of test tracks with a gradual increase in their complexity. In this continuous-control setting, we show that model-based agents capable of learning in imagination, substantially outperform model-free agents with respect to performance, sample efficiency, successful task completion, and generalization. Moreover, we show that the generalization ability of model-based agents strongly depends on the observation-model choice. Finally, we provide extensive empirical evidence for the effectiveness of model-based agents provided with long enough memory horizons in sim2real tasks.

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