基于势能的信用分配用于自动驾驶汽车协作强化学习测试
Potential-based Credit Assignment for Cooperative RL-based Testing of Autonomous Vehicles
AI总结：
本文针对自动驾驶汽车测试中协作强化学习的信用分配难题，提出基于反事实分析的势能奖励塑形方法，并在模拟环境中验证其优于局部和全局奖励方法。
摘要：
尽管自动驾驶汽车（AVs）在一般现实场景中可能表现优异，但它们在部分不可预见情况下的非理性行为引发了严重的安全隐患。本文引入协作强化学习（RL）的概念，以生成针对自动驾驶汽车规划与决策模块的挑战性测试用例。协作强化学习面临的关键挑战之一是信用分配问题，即在交通场景中交互的多个智能体之间，考虑所有参数和时序，恰当地分配奖励并非易事。为解决这一挑战，我们提出了一种受反事实分析启发的新型基于势能的奖励塑形方法，用于解决信用分配问题。在模拟环境中的评估表明，与使用局部奖励和全局奖励的其他方法相比，我们所提出的方法具有优越性。
英文摘要：
While autonomous vehicles (AVs) may perform remarkably well in generic real-life cases, their irrational action in some unforeseen cases leads to critical safety concerns. This paper introduces the concept of collaborative reinforcement learning (RL) to generate challenging test cases for AV planning and decision-making module. One of the critical challenges for collaborative RL is the credit assignment problem, where a proper assignment of rewards to multiple agents interacting in the traffic scenario, considering all parameters and timing, turns out to be non-trivial. In order to address this challenge, we propose a novel potential-based reward-shaping approach inspired by counterfactual analysis for solving the credit-assignment problem. The evaluation in a simulated environment demonstrates the superiority of our proposed approach against other methods using local and global rewards.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
