# Learning Policies for Markov Decision Processes from Data

arXiv: https://arxiv.org/abs/1701.05954 · HTML: https://mlsanity.com/paper/1701.05954/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2017.

## Built on by (most cited first, 3 of 3)

- [Discrete fully probabilistic design: towards a control pipeline for the synthesis of policies from examples](https://mlsanity.com/paper/2112.11210/) (2021)
- [Context-Aware Composition of Agent Policies by Markov Decision Process Entity Embeddings and Agent Ensembles](https://mlsanity.com/paper/2308.14521/) (2023)
- [A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations](https://mlsanity.com/paper/2402.18836/) (2024)

## Abstract (arXiv)

We consider the problem of learning a policy for a Markov decision process consistent with data captured on the state-actions pairs followed by the policy. We assume that the policy belongs to a class of parameterized policies which are defined using features associated with the state-action pairs. The features are known a priori, however, only an unknown subset of them could be relevant. The policy parameters that correspond to an observed target policy are recovered using ℓ1-regularized logistic regression that best fits the observed state-action samples. We establish bounds on the difference between the average reward of the estimated and the original policy (regret) in terms of the generalization error and the ergodic coefficient of the underlying Markov chain. To that end, we combine sample complexity theory and sensitivity analysis of the stationary distribution of Markov chains. Our analysis suggests that to achieve regret within order O(√ε), it suffices to use training sample size on the order of Omega(log n · poly(1/ε)), where n is the number of the features. We demonstrate the effectiveness of our method on a synthetic robot navigation example.