Tootfinder

A Neuro-Symbolic Approach to Multi-Agent RL for Interpretability and Probabilistic Decision Making
Chitra Subramanian, Miao Liu, Naweed Khan, Jonathan Lenchner, Aporva Amarnath, Sarathkrishna Swaminathan, Ryan Riegel, Alexander Gray
https://arxiv.org/abs/2402.13440

A Neuro-Symbolic Approach to Multi-Agent RL for Interpretability and Probabilistic Decision Making
Multi-agent reinforcement learning (MARL) is well-suited for runtime decision-making in optimizing the performance of systems where multiple agents coexist and compete for shared resources. However, applying common deep learning-based MARL solutions to real-world problems suffers from issues of interpretability, sample efficiency, partial observability, etc. To address these challenges, we present an event-driven formulation, where decision-making is handled by distributed co-operative MARL age…