2 Replies to “Efficient Reinforcement Learning in MDPs”

  1. It’s interesting: you translated “Efficient Reinforcement Learning in MDPs” as “EFFICIENT EXPLORATION IN REINFORCEMENT LEARNING”, but surely there are other aspects to the problem. Thoughts on things potentially missing:

    1) Certainly efficient exploration is important, but it is not the be-all, end-all of efficient RL. Key points I think are worth including:

    a) RL with fun-approximation, possibly utilizing hints in the form of baseline distributions or initial policies. Sample complexity and computational complexity with hidden state.
    b) Similarly: exploration isn’t necessary (as in (a) ) if you get help from an expert:

    Exploration and Apprenticeship Learning in Reinforcement Learning,
    Pieter Abbeel and Andrew Y. Ng.

    c) Models aren’t necessary. Your own work on bandit-style bound on the Q-function for exploration.

  2. The translation was something I checked: they are thinking about MDPs. Point (c) is already covered with some discussion at the bottom of page 2.

    I’ll add a bit of discussion about apprenticeship learning—it’s a good point.

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