Temporal and agent abstractions in multiagent reinforcement learning
Danielle M. Clement, Manfred Huber · 2016
A major challenge in the area of multiagent reinforcement learning has been addressing the problem of scale, more specifically the fact that increasing the number of agents in a system dramatically increases both the cost of representing the problem and the cost of calculating a solution. In single agent systems, temporal abstractions in the form of options have been used to address part of the scaling problem, but only limited work exists for multiagent systems, largely limited to cooperative games. This paper presents a formalization of options for multiagent systems and introduces a framework for agent abstraction that treats coalitions executing options analogously to agents with policies, resulting in a lower-dimensional game whose equilibria approximately correspond to equilibria in the higher dimensional game.