Density Matrix MDPs: Structured Probabilistic State Representations for Reinforcement Learning under Demand Uncertainty

Abstract

Introduces density matrix Markov decision processes (DM-MDPs) for structured probabilistic state representations in reinforcement learning under demand uncertainty. Derives theoretical convergence and sample complexity guarantees.

Methodology

1. 01Formalized density matrix state representations for MDPs
2. 02Derived convergence and sample complexity bounds
3. 03Benchmarked on retail demand datasets against standard MDP baselines

Tools Used