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Multi-Agent Determinantal Q-Learning | | Yaodong Yang
; Ying Wen
; Lihuan Chen
; Jun Wang
; Kun Shao
; David Mguni
; Weinan Zhang
; | | Date: |
2 Jun 2020 | | Abstract: | Centralized training with decentralized execution has become an important
paradigm in multi-agent learning. Though practical, current methods rely on
restrictive assumptions to decompose the centralized value function across
agents for execution. In this paper, we eliminate this restriction by proposing
multi-agent determinantal Q-learning. Our method is established on Q-DPP, a
novel extension of determinantal point process (DPP) to multi-agent setting.
Q-DPP promotes agents to acquire diverse behavioral models; this allows a
natural factorization of the joint Q-functions with no need for emph{a priori}
structural constraints on the value function or special network architectures.
We demonstrate that Q-DPP generalizes major solutions including VDN, QMIX, and
QTRAN on decentralizable cooperative tasks. To efficiently draw samples from
Q-DPP, we develop a linear-time sampler with theoretical approximation
guarantee. Our sampler also benefits exploration by coordinating agents to
cover orthogonal directions in the state space during training. We evaluate our
algorithm on multiple cooperative benchmarks; its effectiveness has been
demonstrated when compared with the state-of-the-art. | | Source: | arXiv, 2006.1482 | | Services: | Forum | Review | PDF | Favorites | |
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