1 citations · 1 across the 2 of their papers we have counts for
6 papers
A Survey on Self-play Methods in Reinforcement Learning
Ruize Zhang, Zelai Xu, Chengdong Ma +8
Self-play, a learning paradigm where agents iteratively refine their policies by interacting with historical or concurrent versions of themselves or other evolving agents, has show…
ME-IGM: Individual-Global-Max in Maximum Entropy Multi-Agent Reinforcement Learning
Wen-Tse Chen, Yuxuan Li, Shiyu Huang +2
Multi-agent credit assignment is a fundamental challenge for cooperative multi-agent reinforcement learning (MARL), where a team of agents learn from shared reward signals. The Ind…
MQE: Unleashing the Power of Interaction with Multi-agent Quadruped Environment
Ziyan Xiong, Bo Chen, Shiyu Huang +3
The advent of deep reinforcement learning (DRL) has significantly advanced the field of robotics, particularly in the control and coordination of quadruped robots. However, the com…
LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environments
Junzhe Chen, Xuming Hu, Shuodi Liu +4
Recent advancements in large language models (LLMs) have revealed their potential for achieving autonomous agents possessing human-level intelligence. However, existing benchmarks…
AutoSAT: Automatically Optimize SAT Solvers via Large Language Models
Yiwen Sun, Furong Ye, Xianyin Zhang +4
Conflict-Driven Clause Learning (CDCL) is the mainstream framework for solving the Satisfiability problem (SAT), and CDCL solvers typically rely on various heuristics, which have a…
OpenRL: A Unified Reinforcement Learning Framework
Shiyu Huang, Wentse Chen, Yiwen Sun +2
We present OpenRL, an advanced reinforcement learning (RL) framework designed to accommodate a diverse array of tasks, from single-agent challenges to complex multi-agent systems.…