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Award ID contains: 2240848

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  1. ; ; ; (, Proceedings of AISTATS (28th International Conference on Artificial Intelligence and Statistics))
    We present a new geometric interpretation of Markov Decision Processes (MDPs) with a natural normalization procedure that allows us to adjust the value function at each state without altering the advantage of any action with respect to any policy. This advantage-preserving transformation of the MDP motivates a class of algorithms which we call Reward Balancing, which solve MDPs by iterating through these transformations, until an approximately optimal policy can be trivially found. We provide a convergence analysis of several algorithms in this class, in particular showing that for MDPs for unknown transition probabilities we can improve upon state-of-the-art sample complexity results. 
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    Free, publicly-accessible full text available March 10, 2026
  2. ; ; (, Transactions on Machine Learning Research)
    Accepted Manuscript Full Text Available
  3. ; ; (, IEEE Transactions on Automatic Control)
    Full Text Available 
  4. ; ; (, IEEE Control Systems Letters)
    Full Text Available