4 citations · 6 across the 3 of their papers we have counts for
4 papers
Empirical Policy Optimization for -Player Markov Games
Yuanheng Zhu, Dongbin Zhao, Mengchen Zhao +1
In single-agent Markov decision processes, an agent can optimize its policy based on the interaction with environment. In multi-player Markov games (MGs), however, the interaction…
CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation
Xidong Feng, Chen Chen, Dong Li +3
Practical recommender systems experience a cold-start problem when observed user-item interactions in the history are insufficient. Meta learning, especially gradient based one, ca…
Event-Triggered Multi-agent Reinforcement Learning with Communication under Limited-bandwidth Constraint
Guangzheng Hu, Yuanheng Zhu, Dongbin Zhao +2
Communicating with each other in a distributed manner and behaving as a group are essential in multi-agent reinforcement learning. However, real-world multi-agent systems suffer fr…
Dynamic Horizon Value Estimation for Model-based Reinforcement Learning
Junjie Wang, Qichao Zhang, Dongbin Zhao +2
Existing model-based value expansion methods typically leverage a world model for value estimation with a fixed rollout horizon to assist policy learning. However, the fixed rollou…