7 citations · 26 across the 16 of their papers we have counts for
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cs.LG2023
Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised Learning
Zhaoyi Zhou, Chuning Zhu, Runlong Zhou +3
Off-policy dynamic programming (DP) techniques such as -learning have proven to be important in sequential decision-making problems. In the presence of function approximation, h…
cs.LG2023
A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning
Haozhe Jiang, Qiwen Cui, Zhihan Xiong +2
We investigate learning the equilibria in non-stationary multi-agent systems and address the challenges that differentiate multi-agent learning from single-agent learning. Specific…
cs.LG2023★ 2 cited
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation
Qiwen Cui, Kaiqing Zhang, Simon S. Du
We propose a new model, independent linear Markov game, for multi-agent reinforcement learning with a large state space and a large number of agents. This is a class of Markov game…