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FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis
Aman Sinha · Matthew O'Kelly · Hongrui Zheng · Rahul Mangharam · John Duchi · Russ Tedrake

Tue Jul 14 08:00 AM -- 08:45 AM & Tue Jul 14 08:00 PM -- 08:45 PM (PDT) @ Virtual #None

Balancing performance and safety is crucial to deploying autonomous vehicles in multi-agent environments. In particular, autonomous racing is a domain that penalizes safe but conservative policies, highlighting the need for robust, adaptive strategies. Current approaches either make simplifying assumptions about other agents or lack robust mechanisms for online adaptation. This work makes algorithmic contributions to both challenges. First, to generate a realistic, diverse set of opponents, we develop a novel method for self-play based on replica-exchange Markov chain Monte Carlo. Second, we propose a distributionally robust bandit optimization procedure that adaptively adjusts risk aversion relative to uncertainty in beliefs about opponents’ behaviors. We rigorously quantify the tradeoffs in performance and robustness when approximating these computations in real-time motion-planning, and we demonstrate our methods experimentally on autonomous vehicles that achieve scaled speeds comparable to Formula One racecars.

Author Information

Aman Sinha (Stanford University)
Matthew O'Kelly (University of Pennsylvania)
Hongrui Zheng (University of Pennsylvania)
Rahul Mangharam (University of Pennsylvania)
John Duchi (Stanford University)
Russ Tedrake (MIT)

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