Coordinating multiple agents via reinforcement learning
In this paper, we attempt to use reinforcement learning techniques to solve agent coordination problems in task-oriented environments. The Fuzzy Subjective Task Structure model (FSTS) is presented to model the general agent coordination. We show that an agent coordination problem modeled in FSTS is a Decision-Theoretic Planning (DTP) problem, to which reinforcement learning can be applied. Two learning algorithms, "coarse-grained" and "fine-grained", are proposed to address agents coordination behavior at two different levels. The "coarse-grained" algorithm operates at one level and tackle hard system constraints, and the "fine-grained" at another level and for soft constraints. We argue that it is important to explicitly model and explore coordination-specific (particularly system constraints) information, which underpins the two algorithms and attributes to the effectiveness of the algorithms. The algorithms are formally proved to converge and experimentally shown to be effective.
coordination reinforcement learning task-oriented environment multiagent system