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Creators/Authors contains: "Bastani, O"

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  1. ; ; ; ; (, ICLR)
    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; (, CAV 2023, LNCS 13964)
    Enea, C; Lal, A (Ed.)
    The difficulty of manually specifying reward functions has led to an interest in using linear temporal logic (LTL) to express objec- tives for reinforcement learning (RL). However, LTL has the downside that it is sensitive to small perturbations in the transition probabilities, which prevents probably approximately correct (PAC) learning without additional assumptions. Time discounting provides a way of removing this sensitivity, while retaining the high expressivity of the logic. We study the use of discounted LTL for policy synthesis in Markov decision processes with unknown transition probabilities, and show how to reduce discounted LTL to discounted-sum reward via a reward machine when all discount factors are identical. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; (, Computer Aided Verification - 34th International Conference, {CAV}, 2022, Proceedings, Part II)
    Full Text Available 
  4. ; ; ; (, Springer Festschrift in honor of Prof. Tom Henzinger)
    Accepted Manuscript Full Text Available
  5. ; ; ; (, International Conference on Learning Representations)
    Accepted Manuscript Full Text Available
  6. ; ; (, The 24th International Conference on Artificial Intelligence and Statistics (AISTATS))
    null (Ed.)
    Accepted Manuscript Full Text Available
  7. ; ; (, Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019)
    Accepted Manuscript Full Text Available