Near Optimal Behavior via Approximate State Abstraction

TL;DR

This paper explores approximate state abstractions in reinforcement learning to reduce complexity and maintain near-optimal behavior, providing theoretical guarantees and empirical evidence of effectiveness.

Contribution

It introduces a theoretical framework for approximate abstractions, offering guarantees on behavior quality and demonstrating their practical benefits across environments.

Findings

Approximate abstractions reduce task complexity.

Bounded loss of optimality in derived behaviors.

Theoretical guarantees support practical effectiveness.

Abstract

The combinatorial explosion that plagues planning and reinforcement learning (RL) algorithms can be moderated using state abstraction. Prohibitively large task representations can be condensed such that essential information is preserved, and consequently, solutions are tractably computable. However, exact abstractions, which treat only fully-identical situations as equivalent, fail to present opportunities for abstraction in environments where no two situations are exactly alike. In this work, we investigate approximate state abstractions, which treat nearly-identical situations as equivalent. We present theoretical guarantees of the quality of behaviors derived from four types of approximate abstractions. Additionally, we empirically demonstrate that approximate abstractions lead to reduction in task complexity and bounded loss of optimality of behavior in a variety of environments.