Bandit

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Bandit algorithms

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# Bandit algorithms

## Contextual Bandit
Current implementations  
- UCB1
- LinUCB

simulation example:  
Change Directory to example/scripts and type `./simulation.sh` in command line.  
```python  
./simulation.sh
```
Experiment can be done by changing arguments and Envs in simulation.sh and example/envs/envs.py, example/envs/bandits.py

Original work reference [bgalbraith](https://github.com/bgalbraith/bandits)

## Study guide

### Paper and thesis
- [Lihong Li, Wei Chu, John Langford, Robert E. Schapire, A Contextual-Bandit Approach to Personalized News Article Recommendation](https://arxiv.org/abs/1003.0146)
- [Dai Shi, Exploring Bandit Algorithms for Automatic Content Selection](http://it4bi.univ-tours.fr/it4bi/medias/pdfs/2014_Master_Thesis/IT4BI_2014_submission_30.pdf)
- [Olivier Chapelle, Lihong Li, An Empirical Evaluation of Thompson Sampling](http://papers.nips.cc/paper/4321-an-empirical-evaluation-of-thompson-sampling.pdf)
- [Junpei Komiyama, Junya Honda, Hiroshi Nakagawa, Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays](https://arxiv.org/pdf/1506.00779.pdf)
- [Piette JD, The potential impact of intelligent systems for mobile health self-management support: Monte Carlo simulations of text message support for medication adherence](https://www.ncbi.nlm.nih.gov/pubmed/25082177)

### Lecture notes
- [Contextual bandit models for personalized recommendation](http://chercheurs.lille.inria.fr/ekaufman/ALICIA120514.pdf)
- [Caltech/cs159/linear-bandit-supplement](http://www.yisongyue.com/courses/cs159/lectures/linear_bandit_supplement.pdf)
- [Caltech/cs159/LinUCB](http://www.yisongyue.com/courses/cs159/lectures/LinUCB.pdf)

### Blog posts
- [Contextual Bandits and Exp4 algorithm](http://banditalgs.com/2016/10/14/exp4/)
- [Simple Reinforcement Learning with Tensorflow Part 1.5: Contextual Bandits](https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-1-5-contextual-bandits-bff01d1aad9c)

## TODO
- Thompson Sampling
- Exp3,4

Bandit

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