8 citations · 16 across the 8 of their papers we have counts for
8 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…
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…
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.…
Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models
Haixu Song, Shiyu Huang, Yinpeng Dong +1
The rise of deepfake images, especially of well-known personalities, poses a serious threat to the dissemination of authentic information. To tackle this, we present a thorough inv…
TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play
Fanqi Lin, Shiyu Huang, Tim Pearce +2
Multi-agent football poses an unsolved challenge in AI research. Existing work has focused on tackling simplified scenarios of the game, or else leveraging expert demonstrations. I…
Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation
Xinyi Yang, Shiyu Huang, Yiwen Sun +5
This paper investigates the multi-agent navigation problem, which requires multiple agents to reach the target goals in a limited time. Multi-agent reinforcement learning (MARL) ha…
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization
Wentse Chen, Shiyu Huang, Yuan Chiang +4
Most reinforcement learning algorithms seek a single optimal strategy that solves a given task. However, it can often be valuable to learn a diverse set of solutions, for instance,…