Partially decentralized reinforcement learning in finite, multi-agent Markov decision processes

In this paper, we propose a novel, partially decentralized learning algorithm for the control of finite, multi-agent Markov Decision Process with unknown transition probabilities and reward values. One learning automaton is associated with each agent