Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI)
technique. However, current studies and applications need to address its scalability, non …

A flurry of recent work has demonstrated that pre-trained large language models (LLMs) can
be effective task planners for a variety of single-robot tasks. The planning performance of …

Game theory studies the mathematical models for self-interested individuals. Nash
equilibrium is arguably the most central solution in game theory. While finding the Nash …

Abstract In recent years, Multi-Agent Reinforcement Learning (MARL) techniques have
made significant strides in achieving high asymptotic performance in single task. However …

We study the problem of representation transfer in offline Reinforcement Learning (RL),
where a learner has access to episodic data from a number of source tasks collected a …

Skill discovery has shown significant progress in unsupervised reinforcement learning. This
approach enables the discovery of a wide range of skills without any extrinsic reward, which …

Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

Cooperative multi-agent reinforcement learning (MARL), where agents coordinates with
teammate (s) for a shared goal, may sustain non-stationary caused by the policy change of …

The options framework yields empirical success in long-horizon planning problems of
reinforcement learning (RL). Recent works show that options help improve the sample …

Human-AI coordination aims to develop AI agents capable of effectively coordinating with
human partners, making it a crucial aspect of cooperative multi-agent reinforcement learning …