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On the grid-sampling limit SDE

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arxiv 2410.07778 v1 pith:33XBLH2H submitted 2024-10-10 stat.ML cs.LGmath.PR

classification stat.MLcs.LGmath.PR
keywords grid-samplingcontinuous-timediscussexplorationfurtherintroducedjumpslearning
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In our recent work [3] we introduced the grid-sampling SDE as a proxy for modeling exploration in continuous-time reinforcement learning. In this note, we provide further motivation for the use of this SDE and discuss its wellposedness in the presence of jumps.

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Cited by 1 Pith paper

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  1. Mean-Variance Stackelberg Games with Asymmetric Information

    q-fin.MF 2025-09 conditional novelty 6.0 of 10

    In a two-investor mean-variance Stackelberg game with asymmetric information, the leader's equilibrium randomized strategy is Gaussian and the follower's strategy depends linearly on the leader's observed trades.

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