6 papers
Mastering Multi-Drone Volleyball through Hierarchical Co-Self-Play Reinforcement Learning
Ruize Zhang, Sirui Xiang, Zelai Xu +6
In this paper, we tackle the problem of learning to play 3v3 multi-drone volleyball, a new embodied competitive task that requires both high-level strategic coordination and low-le…
VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play
Zelai Xu, Ruize Zhang, Chao Yu +9
Robot sports, characterized by well-defined objectives, explicit rules, and dynamic interactions, present ideal scenarios for demonstrating embodied intelligence. In this paper, we…
Translate Policy to Language: Flow Matching Generated Rewards for LLM Explanations
Xinyi Yang, Liang Zeng, Heng Dong +6
As humans increasingly share environments with diverse agents powered by RL, LLMs, and beyond, the ability to explain agent policies in natural language is vital for reliable coexi…
Automatic Reward Shaping from Multi-Objective Human Heuristics
Yuqing Xie, Jiayu Chen, Wenhao Tang +3
Designing effective reward functions remains a central challenge in reinforcement learning, especially in multi-objective environments. In this work, we propose Multi-Objective Rew…
AED: Automatic Discovery of Effective and Diverse Vulnerabilities for Autonomous Driving Policy with Large Language Models
Le Qiu, Zelai Xu, Qixin Tan +3
Assessing the safety of autonomous driving policy is of great importance, and reinforcement learning (RL) has emerged as a powerful method for discovering critical vulnerabilities…
Learning Strategic Language Agents in the Werewolf Game with Iterative Latent Space Policy Optimization
Zelai Xu, Wanjun Gu, Chao Yu +2
Large language model (LLM) agents have recently demonstrated impressive capabilities in various domains like open-ended conversation and multi-step decision-making. However, it rem…