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This work introduces ART-RL, an auxiliary randomized formulation with Gaussian policies that turns schedule learning into a continuous-time reinforcement learning problem and proves that the randomized ART-RL formulation is equivalent to ART at the optimizer level, in the sense that its optimal Gaussian policy recovers...
This work takes the exploratory, reinforcement learning (RL) framework of Wang, Zariphopoulou, and Zhou with relaxed controls and entropy regularization with the objective to develop theoretically grounded, efficient and scalable RL algorithms to learn both the optimal value functions and optimal feedback control polic...
This work states that there has been an upsurge of interest and development of RL theory and applications in the communities of stochastic control, stochastic analysis and unsupervised learning.
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