A Decentralized Partially Observable Team Decision Methodology with Delayed Information Sharing
A paper gives decentralized agents a way to learn team policies from delayed shared observations.
The paper studies decentralized partially observable team decision problems with low-rank latent dynamics and unknown system models. Each member uses private information and delayed common information to learn an approximate low-rank Markov decision process and compute a policy with least-squares value iteration. The algorithm requires neither a central coordinator nor centralized training. The authors show that member-side solutions approximate the centralized team policy and derive finite-sample guarantees plus a sample-complexity bound.
- Agents learn approximate low-rank MDPs from local and delayed common information.
- Each member plans with least-squares value iteration and no centralized training.
- Member policies are shown to approximate the centralized team-optimal solution.
- The paper provides finite-sample guarantees and a sample-complexity bound.
From the original article
1 min read at arxiv.orgWe study decentralized partially observable team decision problems with low-rank latent dynamics and unknown system models. The proposed framework combines team-theoretic equivalence with low-rank model representations to address cooperative decision-making in partially observable Markov decision processes without prior knowledge of the transition model.
By Xiaoxing Ren · arxiv.org
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