Optimal regret bounds for selecting the state representation in reinforcement learning

Odalric Ambrym Maillard, Phuong Nguyen, Ronald Ortner, Daniil Ryabko

    Research output: Contribution to conferencePaperpeer-review

    21 Citations (Scopus)

    Abstract

    We consider an agent interacting with an environment in a single stream of actions, observations, and rewards, with no reset. This process is not assumed to be a Markov Decision Process (MDP). Rather, the agent has several representations (mapping histories of past interactions to a discrete state space) of the environment with unknown dynamics, only some of which result in an MDP. The goal is to minimize the average regret criterion against an agent who knows an MDP representation giving the highest optimal reward, and acts optimally in it. Recent regret bounds for this setting are of order O(T 2/3) with an additive term constant yet exponential in some characteristics of the optimal MDP. We propose an algorithm whose regret after T time steps is O(√T), with all constants reasonably small. This is optimal in T since O(√T) is the optimal regret in the setting of learning in a (single discrete) MDP.

    Original languageEnglish
    Pages543-551
    Number of pages9
    Publication statusPublished - 2013
    Event30th International Conference on Machine Learning, ICML 2013 - Atlanta, GA, United States
    Duration: 16 Jun 201321 Jun 2013

    Conference

    Conference30th International Conference on Machine Learning, ICML 2013
    Country/TerritoryUnited States
    CityAtlanta, GA
    Period16/06/1321/06/13

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