State Abstraction in Reinforcement Learning by Eliminating Useless Dimensions

Q-learning and other linear dynamic learning algorithms are subject to Bellman's curse of dimensionality for any realistic learning problem. This paper introduces a framework for satisficing state abstraction -- one that reduces state dimensionality, improving convergence and reducing computational and memory resources -- by eliminating useless state dimensions. Statistical parameters that are dependent on the state and Q-values identify the relevance of a given state space to a task space and allow state elements that contribute least to task learning to be discarded. Empirical results of applying state abstraction to a canonical single-agent path planning task and to a more difficult multi-agent foraging problem demonstrate utility of the proposed methods in improving learning convergence and performance in resource-constrained learning problems.