Dr. rer. nat.

Cheng, Chih-Hong

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  • Publication
    Potential-based Credit Assignment for Cooperative RL-based Testing of Autonomous Vehicles
    ( 2023)
    Ayvaz, Utku
    ;
    Cheng, Chih-Hong
    ;
    Hao, Shen
    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.
  • Publication
    Safeguarding Learning-based Control for Smart Energy Systems with Sampling Specifications
    ( 2023)
    Cheng, Chih-Hong
    ;
    Gupta, Pragya Kirti
    ;
    Venkataramanan, Venkatesh Prasad
    ;
    Hsu, Yun-Fei
    ;
    Burton, Simon
    We study challenges using reinforcement learning in controlling energy systems, where apart from performance requirements, one has additional safety requirements such as avoiding blackouts. We detail how these safety requirements in real-time temporal logic can be strengthened via discretization into linear temporal logic (LTL), such that the satisfaction of the LTL formulae implies the satisfaction of the original safety requirements. The discretization enables advanced engineering methods such as synthesizing shields for safe reinforcement learning as well as formal verification, where for statistical model checking, the probabilistic guarantee acquired by LTL model checking forms a lower bound for the satisfaction of the original real-time safety requirements.