Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms
In this paper, we address two issues of long-standing interest in the reinforcement learning literature. First, what kinds of performance guarantees can be made for Q-learning after only a finite number of actions? Second, what quantitative comparisons can be made between Q-learning and model-based (indirect) approaches, which use experience to estimate next-state distributions for off-line value iteration? 
 
We first show that both Q-learning and the indirect approach enjoy rather rapid convergence to the optimal policy as a function of the number of state transitions observed. In particular, on the order of only (N log(1/e)/e2)(log(N) + loglog(l/e)) transitions are sufficient for both algorithms to come within e of the optimal policy, in an idealized model that assumes the observed transitions are "well-mixed" throughout an N-state MDP. Thus, the two approaches have roughly the same sample complexity. Perhaps surprisingly, this sample complexity is far less than what is required for the model-based approach to actually construct a good approximation to the next-state distribution. The result also shows that the amount of memory required by the model-based approach is closer to N than to N2. 
 
For either approach, to remove the assumption that the observed transitions are well-mixed, we consider a model in which the transitions are determined by a fixed, arbitrary exploration policy. Bounds on the number of transitions required in order to achieve a desired level of performance are then related to the stationary distribution and mixing time of this policy.