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Thompson sampling

In artificial intelligence, Thompson sampling,[1][2] named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists in choosing the action that maximizes the expected reward with respect to a randomly drawn belief.

Contents

DescriptionEdit

Consider a set of contexts  , a set of actions  , and rewards in  . In each round, the player obtains a context  , plays an action   and receives a reward   following a distribution that depends on the context and the issued action. The aim of the player is to play actions such as to maximize the cumulative rewards.

The elements of Thompson sampling are as follows:

  1. a likelihood function  ;
  2. a set   of parameters   of the distribution of  ;
  3. a prior distribution   on these parameters;
  4. past observations triplets  ;
  5. a posterior distribution  , where   is the likelihood function.

Thompson sampling consists in playing the action   according to the probability that it maximizes the expected reward, i.e.

 

where   is the indicator function.

In practice, the rule is implemented by sampling, in each round, parameters   from the posterior  , and choosing the action   that maximizes  , i.e. the expected reward given the sampled parameters, the action and the current context. Conceptually, this means that the player instantiates their beliefs randomly in each round, and then acts optimally according to them. In most practical applications, it is computationally onerous to maintain and sample from a posterior distribution over models. As such, Thompson sampling is often used in conjunction with approximate sampling techniques.[2]

HistoryEdit

Thompson sampling was originally described by Thompson in 1933[1] but has been largely ignored by the artificial intelligence community. It was subsequently rediscovered numerous times independently in the context of reinforcement learning.[3][4][5][6][7][8] A first proof of convergence for the bandit case has been shown in 1997.[3] The first application to Markov decision processes was in 2000.[5] A related approach (see Bayesian control rule) was published in 2010.[4] In 2010 it was also shown that Thompson sampling is instantaneously self-correcting.[8] Asymptotic convergence results for contextual bandits were published in 2011.[6] Nowadays, Thompson Sampling has been widely used in many online learning problems: Thompson sampling has also been applied to A/B testing in website design and online advertising;[9] Thompson sampling has formed the basis for accelerated learning in decentralized decision making;[10] a Double Thompson Sampling (D-TS) [11] algorithm has been proposed for dueling bandits, a variant of traditional MAB, where feedbacks come in the format of pairwise comparison.

Relationship to other approachesEdit

Probability matchingEdit

Probability matching is a decision strategy in which predictions of class membership are proportional to the class base rates. Thus, if in the training set positive examples are observed 60% of the time, and negative examples are observed 40% of the time, the observer using a probability-matching strategy will predict (for unlabeled examples) a class label of "positive" on 60% of instances, and a class label of "negative" on 40% of instances.

Bayesian control ruleEdit

A generalization of Thompson sampling to arbitrary dynamical environments and causal structures, known as Bayesian control rule, has been shown to be the optimal solution to the adaptive coding problem with actions and observations.[4] In this formulation, an agent is conceptualized as a mixture over a set of behaviours. As the agent interacts with its environment, it learns the causal properties and adopts the behaviour that minimizes the relative entropy to the behaviour with the best prediction of the environment's behaviour. If these behaviours have been chosen according to the maximum expected utility principle, then the asymptotic behaviour of the Bayesian control rule matches the asymptotic behaviour of the perfectly rational agent.

The setup is as follows. Let   be the actions issued by an agent up to time  , and let   be the observations gathered by the agent up to time  . Then, the agent issues the action   with probability:[4]

 

where the "hat"-notation   denotes the fact that   is a causal intervention (see Causality), and not an ordinary observation. If the agent holds beliefs   over its behaviors, then the Bayesian control rule becomes

 ,

where   is the posterior distribution over the parameter   given actions   and observations  .

In practice, the Bayesian control amounts to sampling, in each time step, a parameter   from the posterior distribution  , where the posterior distribution is computed using Bayes' rule by only considering the (causal) likelihoods of the observations   and ignoring the (causal) likelihoods of the actions  , and then by sampling the action   from the action distribution  .

Upper-Confidence-Bound (UCB) AlgorithmsEdit

Thompson sampling and upper-confidence bound algorithms share a fundamental property that underlies many of their theoretical guarantees. Roughly speaking, both algorithms allocate exploratory effort to actions that might be optimal and are in this sense "optimistic." Leveraging this property, one can translate regret bounds established for UCB algorithms to Bayesian regret bounds for Thompson sampling[12] or unify regret analysis across both these algorithms and many classes of problems.[13]

ReferencesEdit

  1. ^ a b Thompson, William R. "On the likelihood that one unknown probability exceeds another in view of the evidence of two samples". Biometrika, 25(3–4):285–294, 1933.
  2. ^ a b Daniel J. Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband and Zheng Wen (2018), "A Tutorial on Thompson Sampling", Foundations and Trends® in Machine Learning: Vol. 11: No. 1, pp 1-96. https://www.nowpublishers.com/article/Details/MAL-070
  3. ^ a b J. Wyatt. Exploration and Inference in Learning from Reinforcement. Ph.D. thesis, Department of Artificial Intelligence, University of Edinburgh. March 1997.
  4. ^ a b c d P. A. Ortega and D. A. Braun. "A Minimum Relative Entropy Principle for Learning and Acting", Journal of Artificial Intelligence Research, 38, pages 475–511, 2010.
  5. ^ a b M. J. A. Strens. "A Bayesian Framework for Reinforcement Learning", Proceedings of the Seventeenth International Conference on Machine Learning, Stanford University, California, June 29–July 2, 2000, http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.1701
  6. ^ a b B. C. May, B. C., N. Korda, A. Lee, and D. S. Leslie. "Optimistic Bayesian sampling in contextual-bandit problems". Technical report, Statistics Group, Department of Mathematics, University of Bristol, 2011.
  7. ^ Chapelle, Olivier, and Lihong Li. "An empirical evaluation of thompson sampling." Advances in neural information processing systems. 2011. http://papers.nips.cc/paper/4321-an-empirical-evaluation-of-thompson-sampling
  8. ^ a b O.-C. Granmo. "Solving Two-Armed Bernoulli Bandit Problems Using a Bayesian Learning Automaton", International Journal of Intelligent Computing and Cybernetics, 3 (2), 2010, 207-234.
  9. ^ Ian Clarke. "Proportionate A/B testing", September 22nd, 2011, http://blog.locut.us/2011/09/22/proportionate-ab-testing/
  10. ^ Granmo, O. C.; Glimsdal, S. (2012). "Accelerated Bayesian learning for decentralized two-armed bandit based decision making with applications to the Goore Game". Applied Intelligence. doi:10.1007/s10489-012-0346-z.
  11. ^ Wu, Huasen; Liu, Xin; Srikant, R (2016), Double Thompson Sampling for Dueling Bandits, arXiv:1604.07101, Bibcode:2016arXiv160407101W
  12. ^ Daniel J. Russo and Benjamin Van Roy (2014), "Learning to Optimize Via Posterior Sampling", Mathematics of Operations Research, Vol. 39, No. 4, pp. 1221-1243, 2014. https://pubsonline.informs.org/doi/abs/10.1287/moor.2014.0650
  13. ^ Daniel J. Russo and Benjamin Van Roy (2013), "Eluder Dimension and the Sample Complexity of Optimistic Exploration", Advances in Neural Information Processing Systems 26, pp. 2256-2264. http://papers.nips.cc/paper/4909-eluder-dimension-and-the-sample-complexity-of-optimistic-exploration.pdf