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In [[artificial intelligence]], '''Thompson sampling''',<ref name="ref1"/> 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.


== Description ==
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{{No footnotes|section|date=May 2012}}
 
Consider a set of contexts <math>\mathcal{X}</math>, a set of actions <math>\mathcal{A}</math>, and rewards in <math>\mathbb{R}</math>. In each round, the player obtains a context <math>x \in \mathcal{X}</math>, plays an action <math>a \in \mathcal{A}</math> and receives a reward <math>r \in \mathbb{R}</math> 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:
# a set <math>\Theta</math> of parameters <math>\theta</math>;
# a prior distribution <math>P(\theta)</math> on these parameters;
# past observations triplets <math>\mathcal{D} = \{(x; a; r)\}</math>;
# a likelihood function <math>P(r|\theta,a,x)</math>;
# a posterior distribution <math>P(\theta|\mathcal{D}) \propto P(\mathcal{D}|\theta)P(\theta)</math>, where <math>P(\mathcal{D}|\theta)</math> is the likelihood function.
 
Thompson sampling consists in playing the action <math>a^\ast \in \mathcal{A}</math> according to the probability that it maximizes the expected reward, i.e.
:<math>\int \mathbb{I}[\mathbb{E}(r,a,x,\theta) = \max_{a'} \mathbb{E}(r,a',x,\theta)] P(\theta|\mathcal{D}) \, d\theta,</math>
where <math>\mathbb{I}</math> is the indicator function.
 
In practice, the rule is implemented by sampling, in each round, a parameter <math>\theta^\ast</math> from the posterior <math>P(\theta|\mathcal{D})</math>, and choosing the action <math>a^\ast</math> that maximizes <math>\mathbb{E}[r|\theta^\ast,a^\ast,x]</math>, i.e. the expected reward given the parameter, the action and the current context. Conceptually, this means that the player instantiates his beliefs randomly in each round, and then he acts optimally according to them.
 
== History ==
 
Thompson sampling was originally described in an article by Thompson from 1933 <ref name="ref1"/> but has been largely ignored by the artificial intelligence community. It was subsequently rediscovered numerous times independently in the context of reinforcement learning.<ref name="ref2"/><ref name="ref5"/><ref name="ref6"/><ref name="ref4"/><ref name="ref3"/><ref name="ref7"/> A first proof of convergence for the bandit case has been shown in 1997.<ref name="ref2"/> The first application to [[Markov decision process]]es was in 2000.<ref name="ref6"/> A related approach (see [[Thompson sampling#Bayesian control rule|Bayesian control rule]]) was published in 2010.<ref name="ref5"/> In 2010 it was also shown that Thompson sampling is ''instantaneously self-correcting''.<ref name="ref7"/> Asymptotic convergence results for contextual bandits were published in 2011.<ref name="ref4"/> Thompson sampling has also been applied to A/B testing in website design and online advertising.<ref name="ref9"/> Recently, Thompson sampling has formed the basis for accelerated learning in decentralized decision making.<ref name="ref8"/>
 
== Properties ==
 
=== Convergence ===
 
=== Optimality ===
 
== Relationship to other approaches ==
 
=== Probability matching ===
 
{{See also|Probability matching}}
 
Probability matching is a suboptimal 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 rule ===
 
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.<ref name="ref5"/> 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 <math>a_1, a_2, \ldots, a_T</math> be the actions issued by an agent up to time <math>T</math>, and let <math>o_1, o_2, \ldots, o_T</math> be the observations gathered by the agent up to time <math>T</math>. Then, the agent issues the action <math>a_{T+1}</math> with probability:<ref name="ref5"/>
:<math>P(a_{T+1}|\hat{a}_{1:T}, o_{1:T}),</math>
where the "hat"-notation <math>\hat{a}_t</math> denotes the fact that <math>a_t</math> is a causal intervention (see [[Causality]]), and not an ordinary observation. If the agent holds beliefs <math>\theta \in \Theta</math> over its behaviors, then the Bayesian control rule becomes
:<math>P(a_{T+1}|\hat{a}_{1:T}, o_{1:T}) = \int_{\Theta} P(a_{T+1}|\theta, \hat{a}_{1:T}, o_{1:T}) P(\theta|\hat{a}_{1:T}, o_{1:T}) \, d\theta</math>,
where <math>P(\theta|\hat{a}_{1:T}, o_{1:T})</math> is the posterior distribution over the parameter <math>\theta</math> given actions <math>a_{1:T}</math> and observations <math>o_{1:T}</math>.
 
In practice, the Bayesian control amounts to sampling, in each time step, a parameter <math>\theta^\ast</math> from the posterior distribution <math>P(\theta|\hat{a}_{1:T}, o_{1:T})</math>, where the posterior distribution is computed using Bayes' rule by only considering the (causal) likelihoods of the observations <math>o_1, o_2, \ldots, o_T</math> and ignoring the (causal) likelihoods of the actions <math>a_1, a_2, \ldots, a_T</math>, and then by sampling the action <math>a^\ast_{T+1}</math> from the action distribution <math>P(a_{T+1}|\theta^\ast,\hat{a}_{1:T},o_{1:T})</math>.
 
 
== References ==
 
{{Reflist|refs=
<ref name="ref1">
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.
</ref>
<ref name="ref2">
J. Wyatt. ''Exploration and Inference in Learning from Reinforcement''. Ph.D. thesis, Department of Artificial Intelligence, University of Edinburgh. March 1997.
</ref>
<ref name="ref3">
Chapelle O. and Li, L. "An Empirical Evaluation of Thompson Sampling". NIPS, 2011. {{full|date=April 2013}}
</ref>
<ref name="ref4">
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.
</ref>
<ref name="ref5">
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.
</ref>
<ref name="ref6">
M. J. A. Strens. "A Bayesian Framework for Reinforcement Learning", ''Proceeedings 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
</ref>
<ref name="ref7">
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.
</ref>
<ref name="ref8">{{cite doi|10.1007/s10489-012-0346-z}}
</ref>
<ref name="ref9">Ian Clarke.  "Proportionate A/B testing", September 22nd, 2011, http://blog.locut.us/2011/09/22/proportionate-ab-testing/
</ref>
}}
<!--- Categories --->
 
[[Category:Artificial intelligence]]
[[Category:Heuristic algorithms]]
[[Category:Sequential methods]]
[[Category:Sequential experiments]]
[[Category:Articles created via the Article Wizard]]

Latest revision as of 19:20, 4 March 2014


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