18 citations · 51 across the 6 of their papers we have counts for
15 papers
E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance
Can Chang, Ni Mu, Jiajun Wu +2
A critical challenge in multi-agent reinforcement learning(MARL) is for multiple agents to efficiently accomplish complex, long-horizon tasks. The agents often have difficulties in…
Solving Compositional Reinforcement Learning Problems via Task Reduction
Yunfei Li, Yilin Wu, Huazhe Xu +2
We propose a novel learning paradigm, Self-Imitation via Reduction (SIR), for solving compositional reinforcement learning problems. SIR is based on two core ideas: task reduction…
Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization
Zhenggang Tang, Chao Yu, Boyuan Chen +6
We propose a simple, general and effective technique, Reward Randomization for discovering diverse strategic policies in complex multi-agent games. Combining reward randomization a…
BeBold: Exploration Beyond the Boundary of Explored Regions
Tianjun Zhang, Huazhe Xu, Xiaolong Wang +4
Efficient exploration under sparse rewards remains a key challenge in deep reinforcement learning. To guide exploration, previous work makes extensive use of intrinsic reward (IR).…
Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes
Jiashun Wang, Huazhe Xu, Jingwei Xu +2
Synthesizing 3D human motion plays an important role in many graphics applications as well as understanding human activity. While many efforts have been made on generating realisti…
Multi-Agent Collaboration via Reward Attribution Decomposition
Tianjun Zhang, Huazhe Xu, Xiaolong Wang +4
Recent advances in multi-agent reinforcement learning (MARL) have achieved super-human performance in games like Quake 3 and Dota 2. Unfortunately, these techniques require orders-…