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cs.LG2026
Multi-Agent Model-Based Reinforcement Learning with Joint State-Action Learned Embeddings
Zhizun Wang, David Meger
Learning to coordinate many agents in partially observable and highly dynamic environments requires both informative representations and data-efficient training. To address this ch…
cs.LG2023
VDFD: Multi-Agent Value Decomposition Framework with Disentangled World Model
Zhizun Wang, David Meger
In this paper, we propose a novel model-based multi-agent reinforcement learning approach named Value Decomposition Framework with Disentangled World Model to address the challenge…