PIMbot: Policy and Incentive Manipulation for Multi-Robot Reinforcement Learning in Social Dilemmas

Overview of PIMbot

We present PIMbot, an approach to manipulate the reward function in multi-robot collaboration through two distinct forms: policy and incentive manipulation. PIMBot introduces a new angle for manipulating multi-agent RL social dilemmas through a unique reward function for incentivization. By using our proposed mechanisms, a robot is able to manipulate the social dilemma environment effectively.

Zexin Li
Zexin Li
Ph.D. Student of ECE

I am a Ph.D. student at the University of California, Riverside (UCR). I am fortunate to be advised by Dr. Cong Liu and working with Dr. Yinglun Zhu. I received a bachelor’s degree from the Southern University of Science and Technology (SUSTech) under the advice of Dr. Yuqun Zhang in July 2020. My research interests include but are not limited to the interdisciplinary fields of real-time embedded systems and on-device machine learning. I am actively looking for cooperation in the following topics - (1) deploying machine learning models on real-time embedded devices, (2) system-application co-optimization of machine learning systems, and (3) improving performance robustness in machine learning systems. Feel free to contact me if we share common research interests.