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Deterministic Better Rates for Any Adversarial Deterministic MDP MDPs with adversarial rewards and bandit feedback. (2012)
| Venue: | In Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, |
| Citations: | 1 - 1 self |
BibTeX
@INPROCEEDINGS{Dekel12deterministicbetter,
author = {Ofer Dekel and Elad Hazan},
title = {Deterministic Better Rates for Any Adversarial Deterministic MDP MDPs with adversarial rewards and bandit feedback.},
booktitle = {In Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence,},
year = {2012},
pages = {93--101}
}
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Abstract
Abstract We consider regret minimization in adversarial deterministic Markov Decision Processes (ADMDPs) with bandit feedback. We devise a new algorithm that pushes the state-of-theart forward in two ways: First, it attains a regret of O(T 2/3 ) with respect to the best fixed policy in hindsight, whereas the previous best regret bound was O(T 3/4 ). Second, the algorithm and its analysis are compatible with any feasible ADMDP graph topology, while all previous approaches required additional restrictions on the graph topology.
