University of Virginia: ingy Elsayed aly | logic based reward formation in Multi-Agent Reinforcement Learning

【 title 】Logic-based Reward Shaping for Multi-Agent Reinforcement Learning

【 The author team 】Ingy ElSayed-Aly, Lu Feng

【 Date of publication 】2022.6.17

【 Thesis link 】https://arxiv.org/pdf/2206.08881.pdf

【 Recommended reasons 】 Reinforcement learning (RL) Relies heavily on exploration to learn from the environment and maximize observed rewards . therefore , Must design a reward function , To ensure the best learning from the experience received . Previous studies have combined reward shaping based on automata and logic with environmental assumptions , To provide an automatic mechanism to synthesize reward functions according to tasks . However , How to extend logic based reward formation to Multi-Agent Reinforcement Learning (MARL) The work in this area is still very limited . If the task requires cooperation , The environment will need to consider Federated States to track other agents , Thus suffering from the dimension disaster related to the number of agents . This project explores how to design logic based for different scenarios and tasks MARL Reward formation . This paper presents a novel semi - centralized logic - based MARL Reward formation method , The method is scalable in the number of agents , And evaluate it in multiple scenarios .