Empirical Q-Value Iteration
This work addresses the challenge of reinforcement learning for researchers and practitioners by offering a novel algorithm that avoids stochastic approximation, though it appears incremental in its approach.
The authors tackled the problem of learning the optimal Q-value function in Markov Decision Processes with unknown transition kernels by proposing the empirical Q-value iteration (EQVI) algorithm, which converges to the optimal Q-value function and shows preliminary evidence of faster convergence compared to stochastic approximation-based methods.
We propose a new simple and natural algorithm for learning the optimal Q-value function of a discounted-cost Markov Decision Process (MDP) when the transition kernels are unknown. Unlike the classical learning algorithms for MDPs, such as Q-learning and actor-critic algorithms, this algorithm doesn't depend on a stochastic approximation-based method. We show that our algorithm, which we call the empirical Q-value iteration (EQVI) algorithm, converges to the optimal Q-value function. We also give a rate of convergence or a non-asymptotic sample complexity bound, and also show that an asynchronous (or online) version of the algorithm will also work. Preliminary experimental results suggest a faster rate of convergence to a ball park estimate for our algorithm compared to stochastic approximation-based algorithms.