# A Batch, Off-Policy, Actor-Critic Algorithm for Optimizing the Average Reward

arXiv: https://arxiv.org/abs/1607.05047 · HTML: https://mlsanity.com/paper/1607.05047/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2016.

## Built on by (most cited first, 5 of 5)

- [Estimating Optimal Infinite Horizon Dynamic Treatment Regimes via pT-Learning](https://mlsanity.com/paper/2110.10719/) (2021)
- [Average-reward model-free reinforcement learning: a systematic review and literature mapping](https://mlsanity.com/paper/2010.08920/) (2020)
- [Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach](https://mlsanity.com/paper/2112.04571/) (2021)
- [Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions](https://mlsanity.com/paper/2203.02605/) (2022)
- [Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation](https://mlsanity.com/paper/2310.14976/) (2023)

## 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.