Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings         Authors   Author's Info   Article details         Search    
Deterministic MDPs with Adversarial Rewards and Bandit Feedback
Raman Arora, Ofer Dekel, Ambuj Tewari
We consider a Markov decision process with deterministic state transition dynamics, adversarially generated rewards that change arbitrarily from round to round, and a bandit feedback model in which the decision maker only observes the rewards it receives. In this setting, we present a novel and efficient online decision making algorithm named MarcoPolo. Under mild assumptions on the structure of the transition dynamics, we prove that MarcoPolo enjoys a regret of O(T^(3/4)sqrt(log(T))) against the best deterministic policy in hindsight. Specifically, our analysis does not rely on the stringent unichain assumption, which dominates much of the previous work on this topic.
Pages: 93-101
PS Link:
PDF Link: /papers/12/p93-arora.pdf
AUTHOR = "Raman Arora and Ofer Dekel and Ambuj Tewari",
TITLE = "Deterministic MDPs with Adversarial Rewards and Bandit Feedback",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "93--101"

hosted by DSL   •   site info   •   help