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egreedy
Advanced tools
Readme
A Promises/A+, multi-armed bandit implemented with an epsilon-greedy algorithm.
This implemention is based on Bandit Algorithms for Website Optimization and related empirical research in "Algorithms for the multi-armed bandit problem".
Create a bandit with 3 arms and epsilon 0.25
var Bandit = require('egreedy');
var bandit = new Bandit({
arms: 3,
epsilon: 0.25
});
Select an arm (for exploration or exploitation, according to the algorithm)
bandit.select().then(function (arm) {
console.log('pulled arm=' + arm);
});
Report the reward earned from a chosen arm
bandit.reward(1, 1).then(function (rewards) {
console.log('arm rewards are currently=' + rewards);
});
Install from npm
npm install egreedy --save
Require in your project
var Bandit = require('egreedy');
This algorithm defaults to 2 arms and eplsilon 0.5
var bandit = new Bandit();
The constructor accepts an options object that supports two parameters:
arms
: the number of arms over which the bandit can operateepsilon
: the desired level of epsilon (from 0=never explore/always exploit to 1=always explore/never exploit)var bandit = new Bandit({
arms: 4,
epsilon: 0.75
});
All banditdb algorithms, including this implementation, provide the same Promises/A+ interface.
bandit.select()
Choose an arm to play, according to the specified bandit algorithm.
Arguments
None
Returns
A promise that resolves to a Number corresponding to the associated arm index.
Example
> var Bandit = require('egreedy');
> var bandit = new Bandit();
> bandit.select().then(function (arm) { console.log(arm); });
0
bandit.reward(arm, reward)
Inform the algorithm about the payoff from a given arm.
Arguments
arm
(Integer): the arm index (provided from bandit.select()
)reward
(Number): the observed reward value (which can be 0, to indicate no reward)Returns
A promise that resolves to an Array of the current reward state of each arm; each position in the array corresponds to the associated arm index.
Example
> var Bandit = require('egreedy');
> var bandit = new Bandit();
> bandit.reward(0, 1).then(function (rewards) { console.log(rewards); });
[1, 0]
> bandit.reward(1, 1).then(function (rewards) { console.log(rewards); });
[1, 1]
> bandit.reward(1, 0).then(function (rewards) { console.log(rewards); });
[1, 0.5]
bandit.serialize()
Obtain a persistable JSON object representing the internal state of the algorithm.
Arguments
None
Returns
A promise that resolves to an Object representing parameters required to reconstruct algorithm state.
Example
> var Bandit = require('egreedy');
> var bandit = new Bandit();
> bandit.serialize().then(function (state) { console.log(state); });
{
arms: 2,
epsilon: 0.5,
counts: [ 0, 0 ],
values: [ 0, 0 ]
}
bandit.load(state)
Restore an instance of a bandit to a perviously serialized algorithm state. This method overrides any options parameters passed at instantiation.
Arguments
state
(Object): a serialized algorithm state (provided from bandit.serialize()
)Returns
A promise that resolves to an Array of the current reward state of each arm; each position in the array corresponds to the associated arm index.
Example
> var state = { arms: 2, epsilon: 0.5, counts: [ 1, 2 ], values: [ 1, 0.5 ] };
> var Bandit = require('egreedy');
> var bandit = new Bandit();
> bandit.load(state).then(function (rewards) { console.log(rewards); });
[1, 0.5]
bandit.n
(Number) An instance property representing the total number of recorded reward samples, updated at each bandit.reward()
call.
Example
> var Bandit = require('egreedy');
> var bandit = new Bandit();
> bandit.reward(0, 1).then(function () { console.log(bandit.n); });
1
To run the full unit test suite
npm test
Tests against stochastic methods (e.g. bandit.select()
) are inherently tricky to test with deterministic assertions. The approach here is to iterate across a semi-random set of conditions to verify that each run produces valid output. So, strictly speaking, each call to npm test
is executing a slightly different test suite. At some point, the test suite may be expanded to include a more robust test of the distribution's properties – though because of the number of runs required, would be triggered with an optional flag.
PRs are welcome! For bugs, please include a failing test which passes when your PR is applied.
Currently, this implementation relies on the native Math.random() which uses a seeded "random" number generator. In addition, the underlying calculations often encounter extended floating point numbers. Arm selection is therefore subject to JavaScript's floating point precision limitations. For general information about floating point issues see the floating point guide.
While these factors generally do not impede commercial application, I would consider the implementation suspect in any academic setting.
FAQs
An epsilon-greedy multi-armed bandit algorithm
The npm package egreedy receives a total of 3 weekly downloads. As such, egreedy popularity was classified as not popular.
We found that egreedy demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
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