Bandit problems, Markov decision processes, a smattering of dynamic programming, game theory, optimal control, and online learning the solutions to such problems, esp. reinforcement learning.

Learning, where you must learn an optimal action in response to your stimulus, possibly an optimal “policy” of trying different actions over time, not just an MMSE-minimal prediction from complete data.

Comes in adversarial and stochastic flavours, apparently, although I’ve hit the boundaries of my knowledge there.

## Pseudopolitical diversion

See clickbait bandits.

## Details

TBD.

Conceptually, the base model here is a one- or many-armed poker machine. You can pop coins in, and each time you do you may pull an arm; you might get rewarded. Each arm of the machine might have different returns; but the only way to find out is to play.

How do you choose optimally which arms to pull and when? How much is to work spending to find the arm with the best return on investment, given that it costs to collect more data?

This can be formalised by minimizing *regret* and defining some other terms
and what you get out is a formalized version of Skinnerian learning that you can easily implement
as an algorithm and feel that you have got some satisfyingly optimal properties for.

The thing about whether to choose a new arm when you are on a known good one,
or to switch to another one in hope of it being better,
this is a symbolic one
and it’s called the *exploration/exploitation tradeoff*.

## Extensions

### Reinforcement learning with delayed reward

Richard Xu’s take:

- No data label as in supervised learning, only a
*reward*signal - feedback is delayed
- data are not i.i.d. but sequentially dependent
- agent’s actions affect the data it receives.

New tool by Microsoft: Multi-World Testing (MWT) appears to be an online learning problem that augments its data by re-using the data for offline testing:

… is a toolbox of machine learning technology for principled and efficient experimentation, plausibly applicable to most Microsoft services that interact with customers. In many scenarios, this technology is exponentially more efficient than the traditional A/B testing. The underlying research area, mature and yet very active, is known under many names: “multi-armed bandits”, “contextual bandits”, “associative reinforcement learning”, and “counterfactual evaluation”, among others.

To take an example, suppose one wants to optimize clicks on suggested news stories. To discover what works, one needs to explore over the possible news stories. Further, if the suggested news story can be chosen depending on the visitor’s profile, then one needs to explore over the possible “policies” that map profiles to news stories (and there are exponentially more “policies” than news stories!). Traditional ML fails at this because it does not explore. Whereas MWT allows you to explore continuously, and optimize your decisions using this exploration data.

It has a better introduction here, by John Langford: (cf ABCD15)

Removing the credit assignment problem from reinforcement learning yields the Contextual Bandit setting which we know is generically solvable in the same manner as common supervised learning problems. I know of about a half-dozen real-world successful contextual bandit applications typically requiring the cooperation of engineers and deeply knowledgeable data scientists.

Can we make this dramatically easier? We need a system that explores over appropriate choices with logging of features, actions, probabilities of actions, and outcomes. These must then be fed into an appropriate learning algorithm which trains a policy and then deploys the policy at the point of decision. Naturally, this is what we’ve done and now it can be used by anyone. This drops the barrier to use down to: “Do you have permissions? And do you have a reasonable idea of what a good feature is?”

A key foundational idea is Multiworld Testing: the capability to evaluate large numbers of policies mapping features to action in a manner exponentially more efficient than standard A/B testing. This is used pervasively in the Contextual Bandit literature and you can see it in action for the system we’ve made at Microsoft Research.

## Intros

- Arthur Juliani’s Simple Reinforcement Learning in Tensorflow
- Sébastien Bubeck ‘s intro (complements and update BuCe12)
- Sergey Feldman, Bandits for Recommendation Systems is an EZ-introduction.
- Microsoft’s recommended intro is BuCe12.
- Richard S. Sutton and Andrew G. Barto’s Reinforcement Learning: An Introduction
- Langford’s 2013 NIPS presentation (Lang13)
- Elad Hazan’s origin story for online convex optimisation has just made me realise how new it is. It’s a nice complement to…
- Elad Hazan and Satyan Kale’s tutorial on online convex optimisation.
- The aging, but gentle intro resouce, AI-depot’s Reinforcement learning page

### Bandits-meet-optimisation

Bubeck again: Kernel-based methods for bandit convex optimization, part 1.

### Bandits-meet-evolution

Ben Recht and Roy Frostig, Nesterov’s punctuation equilibrium:

In a landmark new paper by Salimans, Ho, Chen, and Sutskever from OpenAI, (SHCS17) the authors show that a particular class of genetic algorithms (called Evolutionary Strategies) gives excellent performance on a variety of reinforcement learning benchmarks. As optimizers, the application of genetic algorithms raises red flags and usually causes us to close browser windows. But fear not! As we will explain, the particular algorithm deployed happens to be a core method in optimization, and the fact that this method is successful sheds light on the peculiarities of reinforcement learning more than it does about genetic algorithms in general.

### Deep reinforcement learning

Of course, artificial neural networks are a thing in this domain too.

Casual concrete example and intro by Mat Kelcey.

The trick is you approximate the action table in Q-learning using a neural net.

Implementation: keras-rl

## Pseudopolitical diversion: Clickbait bandit problems

An application, esp for contextual bandits.

There is a lot of stuff being done in bandit problems specifically to model consumers and web pages; Therefore I’ll talk about it from that perspective:

The science of treating consumers of modern news media like what they are, near-passive objects of surveillance and control. Because trying to rely on peoples’ rationality and agency to get things done has a poor track record in recent history.

The “bandit problems” phrase comes, by the way, from an extension of the “one armed bandit”, the poker machine, into a mathematical model for exploring the world by pulling on the arms of a poker machine.

mazes we set them instead of dotcom startups.

## Markov decision problems

Bellman and Howard’s classic discrete time control stochastic problem

- Warren Powell’s Introduction to markov decision processes

## POMDP

Too many CPU cycles? Why not look at optimal control in this setting?

“A POMDP is a partially observable Markov decision process. It is a model, originating in the operations research (OR) literature, for describing planning tasks in which the decision maker does not have complete information as to its current state. The POMDP model provides a convenient way of reasoning about tradeoffs between actions to gain reward and actions to gain information.”

## Practicalities

Vowpal Wabbit does contextual bandit learning.:

VW contains a contextual bandit module which allows you to optimize a predictor based on already collected contextual bandit data. In other words, the module does not handle the exploration issue, it assumes it can only use the currently available data previously collected from some “exploration” policy.

## Sequential surrogate interactive model optimisation

Not what I think of here, but it and many other hyperparameter
optimization-type problems have an RL interpretation.
That is, you can use RL to learn the hyper parameters of your deep learning model.
(This is not the same as deep learning *of* RL policies.)
See Sequential surrogate model optimisation for more of that, or the papers
JaTa15, LJDR16a, LJDR16b, FBBZ17.

## To read

- ABCD15
- Agarwal, A., Bird, S., Cozowicz, M., Dudik, M., Langford, J., Li, L., … Slivkins, A. (2015) Introduction to Multi-World Testing.
- Bell57
- Bellman, R. (1957) A Markovian decision process. . DTIC Document
- BeKa61
- Bellman, R., & Kalaba, R. (1961) A note on interrupted stochastic control processes.
*Information and Control*, 4(4), 346–349. DOI. - Bott14
- Bottou, L. (2014) Learning to Interact. . Presented at the Microsoft Techfest
- BPQC13
- Bottou, L., Peters, J., Quiñonero-Candela, J., Charles, D. X., Chickering, D. M., Portugaly, E., … Snelson, E. (2013) Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising.
*Journal of Machine Learning Research*, 14, 3207–3260. - BuCe12
- Bubeck, S., & Cesa-Bianchi, N. (2012) Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems.
*Foundations and Trends® in Machine Learning*, 5(1), 1–122. DOI. - BuMS11
- Bubeck, S., Munos, R., & Stoltz, G. (2011) Pure Exploration in Finitely–Armed and Continuous–Armed Bandits.
*Theoretical Computer Science*, 412, 1832–1852. - BuSl12
- Bubeck, S., & Slivkins, A. (2012) The best of both worlds: stochastic and adversarial bandits.
*ArXiv:1202.4473 [Cs]*. - CeLu06
- Cesa-Bianchi, N., & Lugosi, G. (2006) Prediction, Learning, and Games. . Cambridge ; New York: Cambridge University Press
- DaWa00
- Dayan, P., & Watkins, C. J.(n.d.) Reinforcement Learning. In Encyclopedia of Cognitve Science.
- FBBZ17
- Fernando, C., Banarse, D., Blundell, C., Zwols, Y., Ha, D., Rusu, A. A., … Wierstra, D. (2017) PathNet: Evolution Channels Gradient Descent in Super Neural Networks.
*ArXiv:1701.08734 [Cs]*. - HSWR13
- Hofmann, K., Schuth, A., Whiteson, S., & de Rijke, M. (2013) Reusing Historical Interaction Data for Faster Online Learning to Rank for IR.
- Howa60
- Howard, R. A.(1960) Dynamic Programming and Markov Processes..
- JaTa15
- Jamieson, K., & Talwalkar, A. (2015) Non-stochastic Best Arm Identification and Hyperparameter Optimization.
*ArXiv:1502.07943 [Cs, Stat]*. - KaLM96
- Kaelbling, L. P., Littman, M. L., & Moore, A. W.(1996) Reinforcement Learning: A Survey.
*Journal of Artifical Intelligence Research*, 4. - Krak16
- Krakovsky, M. (2016) Reinforcement Renaissance.
*Commun. ACM*, 59(8), 12–14. DOI. - KrAL16
- Krishnamurthy, A., Agarwal, A., & Langford, J. (2016) Contextual-MDPs for PAC-Reinforcement Learning with Rich Observations.
*ArXiv:1602.02722 [Cs, Stat]*. - Lang13
- Langford, J. (2013) Learning to interact. . Presented at the NIPS 2013
- LCKG15
- Li, L., Chen, S., Kleban, J., & Gupta, A. (2015) Counterfactual Estimation and Optimization of Click Metrics in Search Engines: A Case Study. In Proceedings of the 24th International World Wide Web Conference (WWW’14), Companion Volume. ACM – Association for Computing Machinery
- LCLW11
- Li, L., Chu, W., Langford, J., & Wang, X. (2011) Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms. In Proceedings of the Fourth International Conference on Web Search and Web Data Mining (WSDM-11) (pp. 297–306).
- LJDR16a
- Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., & Talwalkar, A. (2016a) Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits.
*ArXiv:1603.06560 [Cs, Stat]*. - LJDR16b
- Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., & Talwalkar, A. (2016b) Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization.
*ArXiv:1603.06560 [Cs, Stat]*. - PaSa17
- Parisotto, E., & Salakhutdinov, R. (2017) Neural Map: Structured Memory for Deep Reinforcement Learning.
*ArXiv:1702.08360 [Cs]*. - Powe09
- Powell, W. B.(2009) What you should know about approximate dynamic programming.
*Naval Research Logistics (NRL)*, 56(3), 239–249. - SHCS17
- Salimans, T., Ho, J., Chen, X., & Sutskever, I. (2017) Evolution Strategies as a Scalable Alternative to Reinforcement Learning.
*ArXiv:1703.03864 [Cs, Stat]*. - ShYC06
- Shibata, T., Yoshinaka, R., & Chikayama, T. (2006) Probabilistic Generalization of Simple Grammars and Its Application to Reinforcement Learning. In J. L. Balcázar, P. M. Long, & F. Stephan (Eds.), Algorithmic Learning Theory (pp. 348–362). Springer Berlin Heidelberg
- Si04
- Si, J. (2004) Handbook of learning and approximate dynamic programming. (Vol. 2). John Wiley & Sons
- SoXP15
- Song, R., Xie, Y., & Pokutta, S. (2015) Sequential Information Guided Sensing.
*ArXiv:1509.00130 [Cs, Math, Stat]*. - SLLK11
- Strehl, A. L., Langford, J., Li, L., & Kakade, S. M.(2011) Learning from Logged Implicit Exploration Data. In Advances in Neural Information Processing Systems 23 (NIPS-10) (pp. 2217–2225).
- SuBa98
- Sutton, R. S., & Barto, A. G.(1998) Reinforcement learning. . Cambridge, Mass.: MIT Press
- Thru92
- Thrun, S. B.(1992) Efficient Exploration In Reinforcement Learning.