Towards finite-sample convergence of direct reinforcement learning

While direct, model-free reinforcement learning often performs better than model-based approaches in practice, only the latter have yet supported theoretical guarantees for finite-sample convergence. A major difficulty in analyzing the direct approach in an online setting is the absence of a definitive exploration strategy. We extend the notion of admissibility to direct reinforcement learning and show that standard Q-learning with optimistic initial values and constant learning rate is admissible. The notion justifies the use of a greedy strategy that we believe performs very well in practice and holds theoretical significance in deriving finite-sample convergence for direct reinforcement learning. We present empirical evidence that supports our idea.