# Off-policy evaluation for MDPs with unknown structure

arXiv: https://arxiv.org/abs/1502.03255 · HTML: https://mlsanity.com/paper/1502.03255/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2015.

## Built on by (most cited first, 6 of 6)

- [Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization](https://mlsanity.com/paper/2104.13877/) (2021)
- [Factored Adaptation for Non-Stationary Reinforcement Learning](https://mlsanity.com/paper/2203.16582/) (2022)
- [Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach](https://mlsanity.com/paper/2305.18427/) (2023)
- [Model-Invariant State Abstractions for Model-Based Reinforcement Learning](https://mlsanity.com/paper/2102.09850/) (2021)
- [Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning](https://mlsanity.com/paper/2307.09205/) (2023)
- [Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning](https://mlsanity.com/paper/2310.07518/) (2023)

## Abstract (arXiv)

Off-policy learning in dynamic decision problems is essential for providing strong evidence that a new policy is better than the one in use. But how can we prove superiority without testing the new policy? To answer this question, we introduce the G-SCOPE algorithm that evaluates a new policy based on data generated by the existing policy. Our algorithm is both computationally and sample efficient because it greedily learns to exploit factored structure in the dynamics of the environment. We present a finite sample analysis of our approach and show through experiments that the algorithm scales well on high-dimensional problems with few samples.