A Batch, Off-Policy, Actor-Critic Algorithm for Optimizing the Average Reward
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Abstract (arXiv)
We develop an off-policy actor-critic algorithm for learning an optimal policy from a training set composed of data from multiple individuals. This algorithm is developed with a view towards its use in mobile health.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jul 2016.
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Citing papers per year: 2020: 1; 2021: 2; 2022: 1; 2023: 1.
5 papers.
- 2021Estimating Optimal Infinite Horizon Dynamic Treatment Regimes via pT-Learningcited by 26
- 2020Average-reward model-free reinforcement learning: a systematic review and literature mappingcited by 19
- 2021Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approachcited by 6
- 2022Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventionscited by 6
- 2023Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitationcited by 0
- 2023Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation23 Oct 2023
- 2022Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions4 Mar 2022
- 2021Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach8 Dec 2021
- 2021Estimating Optimal Infinite Horizon Dynamic Treatment Regimes via pT-Learning20 Oct 2021
- 2020Average-reward model-free reinforcement learning: a systematic review and literature mapping18 Oct 2020