Optimistic Regret Bounds for Online Learning in Adversarial Markov Decision Processes
This work addresses the challenge of overly conservative regret analysis in decision-making applications like robotics and recommendation systems, offering a more optimistic approach for scenarios where cost changes are not fully adversarial.
The paper tackles the problem of pessimistic regret bounds in Adversarial Markov Decision Processes (AMDPs) by introducing a new variant that uses cost predictors, achieving a sublinear optimistic regret bound with high probability that degrades gracefully with predictor accuracy.
The Adversarial Markov Decision Process (AMDP) is a learning framework that deals with unknown and varying tasks in decision-making applications like robotics and recommendation systems. A major limitation of the AMDP formalism, however, is pessimistic regret analysis results in the sense that although the cost function can change from one episode to the next, the evolution in many settings is not adversarial. To address this, we introduce and study a new variant of AMDP, which aims to minimize regret while utilizing a set of cost predictors. For this setting, we develop a new policy search method that achieves a sublinear optimistic regret with high probability, that is a regret bound which gracefully degrades with the estimation power of the cost predictors. Establishing such optimistic regret bounds is nontrivial given that (i) as we demonstrate, the existing importance-weighted cost estimators cannot establish optimistic bounds, and (ii) the feedback model of AMDP is different (and more realistic) than the existing optimistic online learning works. Our result, in particular, hinges upon developing a novel optimistically biased cost estimator that leverages cost predictors and enables a high-probability regret analysis without imposing restrictive assumptions. We further discuss practical extensions of the proposed scheme and demonstrate its efficacy numerically.