5 papers · 1 filter
Learning what to say and how precisely: Efficient Communication via Differentiable Discrete Communication Learning
Aditya Kapoor, Yash Bhisikar, Benjamin Freed +2
Effective communication in multi-agent reinforcement learning (MARL) is critical for success but constrained by bandwidth, yet past approaches have been limited to complex gating m…
Re-evaluating LLM-based Heuristic Search: A Case Study on the 3D Packing Problem
Guorui Quan, Mingfei Sun, Manuel López-Ibáñez
The art of heuristic design has traditionally been a human pursuit. While Large Language Models (LLMs) can generate code for search heuristics, their application has largely been c…
Generating Query-Relevant Document Summaries via Reinforcement Learning
Nitin Yadav, Changsung Kang, Hongwei Shang +1
E-commerce search engines often rely solely on product titles as input for ranking models with latency constraints. However, this approach can result in suboptimal relevance predic…
Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning
Beining Zhang, Aditya Kapoor, Mingfei Sun
Multi-agent reinforcement learning (MARL) often relies on \emph{parameter sharing (PS)} to scale efficiently. However, purely shared policies can stifle each agent's unique special…
Redistributing Rewards Across Time and Agents for Multi-Agent Reinforcement Learning
Aditya Kapoor, Kale-ab Tessera, Mayank Baranwal +4
Credit assignmen, disentangling each agent's contribution to a shared reward, is a critical challenge in cooperative multi-agent reinforcement learning (MARL). To be effective, cre…