109 citations · 288 across the 16 of their papers we have counts for
6 papers · 1 filter
Fewer May Be Better: Enhancing Offline Reinforcement Learning with Reduced Dataset
Yiqin Yang, Quanwei Wang, Chenghao Li +8
Offline reinforcement learning (RL) represents a significant shift in RL research, allowing agents to learn from pre-collected datasets without further interaction with the environ…
Never Explore Repeatedly in Multi-Agent Reinforcement Learning
Chenghao Li, Tonghan Wang, Chongjie Zhang +1
In the realm of multi-agent reinforcement learning, intrinsic motivations have emerged as a pivotal tool for exploration. While the computation of many intrinsic rewards relies on…
Learning Diverse Risk Preferences in Population-based Self-play
Yuhua Jiang, Qihan Liu, Xiaoteng Ma +5
Among the great successes of Reinforcement Learning (RL), self-play algorithms play an essential role in solving competitive games. Current self-play algorithms optimize the agent…
Containerized Distributed Value-Based Multi-Agent Reinforcement Learning
Siyang Wu, Tonghan Wang, Chenghao Li +2
Multi-agent reinforcement learning tasks put a high demand on the volume of training samples. Different from its single-agent counterpart, distributed value-based multi-agent reinf…
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning
Chenghao Li, Tonghan Wang, Chengjie Wu +3
Recently, deep multi-agent reinforcement learning (MARL) has shown the promise to solve complex cooperative tasks. Its success is partly because of parameter sharing among agents.…
Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning
Xiaoteng Ma, Yiqin Yang, Chenghao Li +3
Value-based methods of multi-agent reinforcement learning (MARL), especially the value decomposition methods, have been demonstrated on a range of challenging cooperative tasks. Ho…