Online Markov Decision Processes under Bandit Feedback. Neu, G., György, A., Szepesvári, C., & Antos, A. *IEEE Transactions on Automatic Control*, 59(3):676–691, 12, 2014.

Paper abstract bibtex

Paper abstract bibtex

We consider online learning in finite stochastic Markovian environments where in each time step a new reward function is chosen by an oblivious adversary. The goal of the learning agent is to compete with the best stationary policy in hindsight in terms of the total reward received. Specifically, in each time step the agent observes the current state and the reward associated with the last transition, however, the agent does not observe the rewards associated with other state-action pairs. The agent is assumed to know the transition probabilities. The state of the art result for this setting is an algorithm with an expected regret of $O(T^{2/3}ln T)$. In this paper, assuming that stationary policies mix uniformly fast, we show that after $T$ time steps, the expected regret of this algorithm (more precisely, a slightly modified version thereof) is $O(T^{1/2}ln T)$, giving the first rigorously proven, essentially tight regret bound for the problem.

@article{NeGySzA13, abstract = {We consider online learning in finite stochastic Markovian environments where in each time step a new reward function is chosen by an oblivious adversary. The goal of the learning agent is to compete with the best stationary policy in hindsight in terms of the total reward received. Specifically, in each time step the agent observes the current state and the reward associated with the last transition, however, the agent does not observe the rewards associated with other state-action pairs. The agent is assumed to know the transition probabilities. The state of the art result for this setting is an algorithm with an expected regret of $O(T^{2/3}ln T)$. In this paper, assuming that stationary policies mix uniformly fast, we show that after $T$ time steps, the expected regret of this algorithm (more precisely, a slightly modified version thereof) is $O(T^{1/2}ln T)$, giving the first rigorously proven, essentially tight regret bound for the problem.}, author = {Neu, G. and Gy{\"o}rgy, A. and Szepesv{\'a}ri, Cs. and Antos, A.}, date = {2014-01}, date-added = {2013-11-29 18:51:13 +0200}, date-modified = {2015-03-02 01:15:25 +0000}, journal = {IEEE Transactions on Automatic Control}, keywords = {online learning, adversarial setting, finite MDPs, recurrent MDPs, theory}, month = {12}, number = {3}, pages = {676--691}, title = {Online Markov Decision Processes under Bandit Feedback}, url_paper = {mdptac13.pdf}, volume = {59}, year = {2014}}

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G.","György, A.","Szepesvári, C.","Antos, A."],"bibdata":{"bibtype":"article","type":"article","abstract":"We consider online learning in finite stochastic Markovian environments where in each time step a new reward function is chosen by an oblivious adversary. The goal of the learning agent is to compete with the best stationary policy in hindsight in terms of the total reward received. Specifically, in each time step the agent observes the current state and the reward associated with the last transition, however, the agent does not observe the rewards associated with other state-action pairs. The agent is assumed to know the transition probabilities. The state of the art result for this setting is an algorithm with an expected regret of $O(T^{2/3}ln T)$. In this paper, assuming that stationary policies mix uniformly fast, we show that after $T$ time steps, the expected regret of this algorithm (more precisely, a slightly modified version thereof) is $O(T^{1/2}ln T)$, giving the first rigorously proven, essentially tight regret bound for the problem.","author":[{"propositions":[],"lastnames":["Neu"],"firstnames":["G."],"suffixes":[]},{"propositions":[],"lastnames":["György"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Szepesvári"],"firstnames":["Cs."],"suffixes":[]},{"propositions":[],"lastnames":["Antos"],"firstnames":["A."],"suffixes":[]}],"date":"2014-01","date-added":"2013-11-29 18:51:13 +0200","date-modified":"2015-03-02 01:15:25 +0000","journal":"IEEE Transactions on Automatic Control","keywords":"online learning, adversarial setting, finite MDPs, recurrent MDPs, theory","month":"12","number":"3","pages":"676–691","title":"Online Markov Decision Processes under Bandit Feedback","url_paper":"mdptac13.pdf","volume":"59","year":"2014","bibtex":"@article{NeGySzA13,\n\tabstract = {We consider online learning in finite stochastic Markovian environments where in each time step a new reward function is chosen by an oblivious adversary. The goal of the learning agent is to compete with the best stationary policy in hindsight in terms of the total reward received. Specifically, in each time step the agent observes the current state and the reward associated with the last transition, however, the agent does not observe the rewards associated with other state-action pairs. The agent is assumed to know the transition probabilities. The state of the art result for this setting is an algorithm with an expected regret of $O(T^{2/3}ln T)$. In this paper, assuming that stationary policies mix uniformly fast, we show that after $T$ time steps, the expected regret of this algorithm (more precisely, a slightly modified version thereof) is $O(T^{1/2}ln T)$, giving the first rigorously proven, essentially tight regret bound for the problem.},\n\tauthor = {Neu, G. and Gy{\\\"o}rgy, A. and Szepesv{\\'a}ri, Cs. and Antos, A.},\n\tdate = {2014-01},\n\tdate-added = {2013-11-29 18:51:13 +0200},\n\tdate-modified = {2015-03-02 01:15:25 +0000},\n\tjournal = {IEEE Transactions on Automatic Control},\n\tkeywords = {online learning, adversarial setting, finite MDPs, recurrent MDPs, theory},\n\tmonth = {12},\n\tnumber = {3},\n\tpages = {676--691},\n\ttitle = {Online Markov Decision Processes under Bandit Feedback},\n\turl_paper = {mdptac13.pdf},\n\tvolume = {59},\n\tyear = {2014}}\n\n","author_short":["Neu, G.","György, A.","Szepesvári, C.","Antos, A."],"key":"NeGySzA13","id":"NeGySzA13","bibbaseid":"neu-gyrgy-szepesvri-antos-onlinemarkovdecisionprocessesunderbanditfeedback-2014","role":"author","urls":{" paper":"https://www.ualberta.ca/~szepesva/papers/mdptac13.pdf"},"keyword":["online learning","adversarial setting","finite MDPs","recurrent MDPs","theory"],"metadata":{"authorlinks":{"szepesvári, c":"https://sites.ualberta.ca/~szepesva/pubs.html"}},"html":""},"bibtype":"article","biburl":"https://www.ualberta.ca/~szepesva/papers/p2.bib","creationDate":"2020-03-08T20:45:59.850Z","downloads":5,"keywords":["online learning","adversarial setting","finite mdps","recurrent mdps","theory"],"search_terms":["online","markov","decision","processes","under","bandit","feedback","neu","györgy","szepesvári","antos"],"title":"Online Markov Decision Processes under Bandit Feedback","year":2014,"dataSources":["dYMomj4Jofy8t4qmm","Ciq2jeFvPFYBCoxwJ","v2PxY4iCzrNyY9fhF","cd5AYQRw3RHjTgoQc"]}