10 citations · 17 across the 4 of their papers we have counts for
4 papers
A Game-Theoretic Perspective of Generalization in Reinforcement Learning
Chang Yang, Ruiyu Wang, Xinrun Wang +1
Generalization in reinforcement learning (RL) is of importance for real deployment of RL algorithms. Various schemes are proposed to address the generalization issues, including tr…
PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations
Tong Sang, Hongyao Tang, Yi Ma +5
Deep Reinforcement Learning (DRL) has been a promising solution to many complex decision-making problems. Nevertheless, the notorious weakness in generalization among environments…
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration
Pengyi Li, Hongyao Tang, Tianpei Yang +8
Learning to collaborate is critical in Multi-Agent Reinforcement Learning (MARL). Previous works promote collaboration by maximizing the correlation of agents' behaviors, which is…
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework
Xiaotian Hao, Hangyu Mao, Weixun Wang +5
The state space in Multiagent Reinforcement Learning (MARL) grows exponentially with the agent number. Such a curse of dimensionality results in poor scalability and low sample eff…