SmoothCruiser achieves O~(1/epsilon^4) problem-independent sample complexity for value estimation in entropy-regularized MDPs and games via a generative model.
https://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/1880/2049 Integrating sample-based planning and model-based reinforcement learning
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Planning in entropy-regularized Markov decision processes and games
SmoothCruiser achieves O~(1/epsilon^4) problem-independent sample complexity for value estimation in entropy-regularized MDPs and games via a generative model.