Planning in entropy-regularized Markov decision processes and games
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Abstract (arXiv)
We propose SmoothCruiser, a new planning algorithm for estimating the value function in entropy-regularized Markov decision processes and two-player games, given a generative model of the environment. SmoothCruiser makes use of the smoothness of the Bellman operator promoted by the regularization to achieve problem-independent sample complexity of order O~(1/epsilon^4) for a desired accuracy epsilon, whereas for non-regularized settings there are no known algorithms with guaranteed polynomial sample complexity in the worst case.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 27 references that are in the map. First posted on arXiv 21 Apr 2026.
Builds on
13 of 27 references.
References · 13
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
- 2015Trust Region Policy Optimizationcited by 2,580
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
- 2017Reinforcement Learning with Deep Energy-Based Policiescited by 669
- 2019A Theory of Regularized Markov Decision Processescited by 286
- 2017A unified view of entropy-regularized Markov decision processescited by 182
- 2017Equivalence Between Policy Gradients and Soft Q-Learningcited by 173
- 2014Bandit Algorithms for Tree Searchcited by 25
- 2017Monte-Carlo Tree Search by Best Arm Identificationcited by 13
- 2017Structured Best Arm Identification with Fixed Confidencecited by 11
- 2026Blazing the trails before beating the path: Sample-efficient Monte-Carlo planningcited by 5
- 2019Practical Open-Loop Optimistic Planningcited by 4
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Citing papers per year: 2026: 2.
2 papers.