Cooperative trajectory planning methods for automated vehicles can solve traffic scenarios that require a high degree of cooperation between traffic participants. However, for cooperative systems to integrate into human-centered traffic, the automated systems must behave human-like so that humans can anticipate the system’s decisions. While Reinforcement Learning has made remarkable progress in solving the decision-making part, it is non-trivial to parameterize a reward model that yields predictable actions. This work employs feature-based Maximum Entropy Inverse Reinforcement Learning combined with Monte Carlo Tree Search to learn reward models that maximize the likelihood of recorded multi-agent cooperative expert trajectories. The evaluation demonstrates that the approach can recover a reasonable reward model that mimics the expert and performs similarly to a manually tuned baseline reward model.


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    Title :

    Learning Reward Models for Cooperative Trajectory Planning with Inverse Reinforcement Learning and Monte Carlo Tree Search


    Contributors:


    Publication date :

    2022-06-05


    Size :

    915869 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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