# Relax but stay in control: from value to algorithms for online Markov decision processes

@article{Guan2013RelaxBS, title={Relax but stay in control: from value to algorithms for online Markov decision processes}, author={Peng Guan and Maxim Raginsky and Rebecca M. Willett}, journal={ArXiv}, year={2013}, volume={abs/1310.7300} }

Online learning algorithms are designed to perform in non-stationary environments, but generally there is no notion of a dynamic state to model constraints on current and future actions as a function of past actions. State-based models are common in stochastic control settings, but commonly used frameworks such as Markov Decision Processes (MDPs) assume a known stationary environment. In recent years, there has been a growing interest in combining the above two frameworks and considering an MDP…

## 2 Citations

### From minimax value to low-regret algorithms for online Markov decision processes

- Computer Science2014 American Control Conference
- 2014

This paper builds on recent results of Rakhlin et al. to give a general framework for deriving algorithms in an MDP setting with arbitrarily changing costs that leads to a unifying view of existing methods and provides a general procedure for constructing new ones.

### Topics in Online Markov Decision Processes

- Art
- 2015

Topics in Online Markov Decision Processes by Peng Guan Department of Electrical and Computer Engineering Duke University are topics for research and teaching.

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