Relative Q-Learning for Average-Reward Markov Decision Processes With Continuous States

Markov decision processes (MDPs) are widely used for modeling sequential decision-making problems under uncertainty. We propose an online algorithm for solving a class of average-reward MDPs with continuous state spaces in a model-free setting. The algorithm combines the classical relative Q-learning with an asynchronous averaging procedure, which permits the Q-value estimate at a state-action pair to be updated based on observations at other neighboring pairs sampled in subsequent iterations. These point estimates are then retained and used for constructing an interpolation-based function approximator that predicts the Q-function values at unexplored state-action pairs. We show that with probability one the sequence of function approximators converges to the optimal Q-function up to a constant. Numerical results on a simple benchmark example are reported to illustrate the algorithm.