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Reward (Mis)design for Autonomous Driving
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This article considers the problem of diagnosing certain common errors in reward design. Its insights are also applicable to the design of cost functions and performance metrics more generally. To diagnose common errors, we develop 8 simple sanity checks for identifying flaws in reward functions. These sanity checks are applied to reward functions from past work on reinforcement learning (RL) for autonomous driving (AD), revealing near-universal flaws in reward design for AD that might also exist pervasively across reward design for other tasks. Lastly, we explore promising directions that may aid the design of reward functions for AD in subsequent research, following a process of inquiry that can be adapted to other domains.
Forward citations
Cited by 2 Pith papers
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ROAD: Responsibility-Oriented Reward Design for Reinforcement Learning in Autonomous Driving
A responsibility-aware crash penalty, built from a traffic-law knowledge graph and a vision-language blame classifier, improves MetaDrive success rates and shifts reported collision blame away from the ego vehicle.
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Residual Reward Models for Preference-based Reinforcement Learning
Combining a hand-designed or learned prior reward with a preference-trained residual improves sample efficiency and final performance in preference-based reinforcement learning.
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