Logistic Q-Learning
This work addresses policy evaluation for reinforcement learning practitioners, offering a theoretically sound method, but it appears incremental as it builds closely on existing REPS algorithms.
The authors tackled the problem of policy evaluation in reinforcement learning by proposing QREPS, a new algorithm derived from a regularized linear-programming formulation, which introduces a convex loss function as an alternative to squared Bellman error and demonstrates effectiveness on benchmark problems.
We propose a new reinforcement learning algorithm derived from a regularized linear-programming formulation of optimal control in MDPs. The method is closely related to the classic Relative Entropy Policy Search (REPS) algorithm of Peters et al. (2010), with the key difference that our method introduces a Q-function that enables efficient exact model-free implementation. The main feature of our algorithm (called QREPS) is a convex loss function for policy evaluation that serves as a theoretically sound alternative to the widely used squared Bellman error. We provide a practical saddle-point optimization method for minimizing this loss function and provide an error-propagation analysis that relates the quality of the individual updates to the performance of the output policy. Finally, we demonstrate the effectiveness of our method on a range of benchmark problems.