Exploiting Fast Decaying and Locality in Multi-Agent MDP with Tree Dependence Structure
This paper considers a multi-agent Markov Decision Process (MDP), where there are n agents and each agent i is associated with a state si and action ai taking values from a finite set. Though the global state space size and action space size are exponential in n, we impose local dependence structures and focus on local policies that only depend on local states, and we propose a method that finds nearly optimal local policies in polynomial time (in n) when the dependence structure is a one directional tree. The algorithm builds on approximated reward functions which are evaluated using locally truncated Markov process. Further, under some special conditions, we prove that the gap between the approximated reward function and the true reward function is decaying exponentially fast as the length of the truncated Markov process gets longer. The intuition behind this is that under some assumptions, the effect of agent interactions decays exponentially in the distance between agents, which we term "fast decaying property". Results in this paper are our preliminary steps towards designing efficient reinforcement learning algorithms with optimality guarantees in large multi-agent MDP problems whose state (action) space size is exponentially large in n.