On the grid-sampling limit SDE
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 10 Oct 2024.
Builds on
8 of 24 references.
References · 8
- 2021Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithmscited by 76
- 2022q-Learning in Continuous Timecited by 68
- 2021Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approachcited by 62
- 2020Entropy Regularization for Mean Field Games with Learningcited by 50
- 2020Regularity and stability of feedback relaxed controlscited by 12
- 2020Exploratory LQG Mean Field Games with Entropy Regularizationcited by 11
- 2023Entropy-Regularized Mean-Variance Portfolio Optimization with Jumpscited by 5
- 2024A random measure approach to reinforcement learning in continuous timecited by 1
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.