Explainable Deterministic MDPs

TL;DR

This paper introduces a method for analyzing certain deterministic MDPs that reveals reward sources and policy behavior without fully computing the value or policy functions, enabling explainability of decision-making.

Contribution

The paper presents a novel approach to relate optimal policies to reward sources in deterministic MDPs without full value function computation, enhancing interpretability.

Findings

Can identify reward collection patterns for initial states

Maps state space regions dominated by specific rewards

Explains all actions without full policy computation

Abstract

We present a method for a certain class of Markov Decision Processes (MDPs) that can relate the optimal policy back to one or more reward sources in the environment. For a given initial state, without fully computing the value function, q-value function, or the optimal policy the algorithm can determine which rewards will and will not be collected, whether a given reward will be collected only once or continuously, and which local maximum within the value function the initial state will ultimately lead to. We demonstrate that the method can be used to map the state space to identify regions that are dominated by one reward source and can fully analyze the state space to explain all actions. We provide a mathematical framework to show how all of this is possible without first computing the optimal policy or value function.