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Creators/Authors contains: "Aspeel, Antoine"

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  1. ; ; ; ; (, Proceedings of the 6th Annual Learning for Dynamics & Control Conference, PMLR 242:1266-1277)
    Inverse reinforcement Learning (IRL) has emerged as a powerful paradigm for extracting expert skills from observed behavior, with applications ranging from autonomous systems to humanrobot interaction. However, the identifiability issue within IRL poses a significant challenge, as multiple reward functions can explain the same observed behavior. This paper provides a linear algebraic characterization of several identifiability notions for an entropy-regularized finite horizon Markov decision process (MDP). Moreover, our approach allows for the seamless integration of prior knowledge, in the form of featurized reward functions, to enhance the identifiability of IRL problems. The results are demonstrated with experiments on a grid world environment. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; (, IEEE Control Systems Letters)
    Koopman liftings have been successfully used to learn high dimensional linear approximations for autonomous systems for prediction purposes, or for control systems for leveraging linear control techniques to control nonlinear dynamics. In this paper, we show how learned Koopman approximations can be used for state-feedback correct-by-construction control. To this end, we introduce the Koopman over-approximation, a (possibly hybrid) lifted representation that has a simulation-like relation with the underlying dynamics. Then, we prove how successive application of controlled predecessor operation in the lifted space leads to an implicit backward reachable set for the actual dynamics. Finally, we demonstrate the approach on two nonlinear control examples with unknown dynamics. 
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    Full Text Available 
  3. ; ; ; (, IEEE Control Systems Letters)
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