Thesis Proposal: Scaling Reinforcement Learning Systems

Abstract

Thesis proposal.Reinforcement learning systems are interesting because they meet three major criteria for animate control, namely: competence, responsiveness, and autonomous adaptability. Unfortunately, these systems have not been scaled to complex task domains. For my thesis I propose to study three separate problems that arise when scaling reinforcement learning systems to larger task domains. These are: the propagation problem, the transfer problem, and the attention problem. The propagation problem arises when the number of states in the problem domain is scaled and the distance the system must go for reinforcement is increased. The transfer problem occurs when reinforcement learning systems are applied to problem solving tasks where its desirable to transfer knowledge useful for solving one problem to another. The attention problem arises when a system with a fixed length input vector is applied to a task domains containing an arbitrary number of objects. Each of these problems are discussed along with possible approaches for their solution. A schedule for performing the research is also given