A Continuous-Time Reinforcement Learning Framework for Fine-Tuning Discrete Diff
We formulate reinforcement learning (RL) in continuous time with discrete state spaces and possibly arbitrary action spaces via a stochastic control approach, where the state dynamics are modeled as a controlled continuous-time Markov chain
https://arxiv.org/abs/2607.14522v1 ↗Thesis fit
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ai
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Z
Zikun Zhang
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J
Jiayuan Sheng
1.0
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D
David D. Yao
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Wenpin Tang
1.0
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