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On the grid-sampling limit SDE
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On the grid-sampling limit SDE
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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.
Forward citations
Cited by 2 Pith papers
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Continuous-time q-learning for mean-field control with common noise, part-I: Theoretical foundations
Establishes existence and uniqueness for optimal policies in continuous-time entropy-regularized mean-field control with common noise via an integrated q-function, plus explicit Gaussian characterization in the LQ setting.
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Discretization error from regularized Reinforcement Learning to continuous-time stochastic control
Derives quantitative convergence rates for the gap between optimal policies from regularized discrete-time Bellman equations and true optimal controls in underlying continuous-time stochastic problems.
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