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banditsbook-scala

sample_plot

This repository is inspired by johnmyleswhite/BanditsBook.

This contains Scala implementations (with breeze and cats) of several standard algorithms for solving the Multi-Armed Bandits Problem, including:

  • epsilon-Greedy
  • Softmax (Boltzmann)
  • UCB1
  • UCB2
  • Hedge
  • Exp3
  • annealing versions of aboves

It also contains code that provides a testing framework for bandit algorithms based around simple Monte Carlo simulations.

Languages and design overview

All implementations are in Scala. If you're interested in other language, please see johnmyleswhite/BanditsBook.

This bandits algorithms are implemented by functional and stateless style. Algorithm behavior is modeled as State Monad by using cats (please see Demo.scala).

Implementing bandits algorithm needs to linear algebra(vector/matrix calculations) and probability calculations. This implementation uses breeze for those features.

Getting Started

To try out this code, you can go to Demo.scala and then run the demo.

// run the demo written in procedural manner (but it is stateless.)
sbt "run-main com.github.everpeace.banditsbook.Demo"

// run the demo written in monadic manner
sbt "run-main com.github.everpeace.banditsbook.DemoMonadic"

You should step through that code line-by-line to understand what the functions are doing. The book provides more in-depth explanations of how the algorithms work.

Simulations

This repository includes some handy scripts to run simulations.

  • Prerequisites:
    • sbt
    • R (for plotting simulation results)

To run simulations, just hit the commands

// this takes some time, you can enjoy a cup of coffee :-).
$ cd banditsbook-scala
$ ./run_test.sh all

This will executes to run simulations on the configurations defined in application.conf and generate graphs of simulation results to output directory like an image on the top. Please note that all arms are simulated by Bernoulli distributions.

Adding New Algorithms: API Expectations

Algorithm is defined as below:

// Reward         : type of reward which this algorithm work for.
// AlgorithmState : type of state which the algorithm handles.
abstract class Algorithm[Reward, AlgorithmState] {

  // The method works for initialization.
  // Given arms, returns state value of this algorithm
  def initialState(arms: Seq[Arm[Reward]]): AlgorithmState

  // The method that returns the index of the Arm
  // that the algorithm selects on the current play.
  def selectArm(arms: Seq[Arm[Reward]], state: AlgorithmState): Int

  // The method calculates next state of the algorithm
  // in response to its most recently selected arm's reward.
  def updateState(arms: Seq[Arm[Reward]], state: AlgorithmState, chosen: Int, reward: Reward): AlgorithmState

  //
  // State Monadic Values:
  //   These values are induced from above methods.
  //   This means you can get a monadic algorithm
  //   instance for free!
  //
  import cats.data.State
  import State._
  def selectArm: State[(Seq[Arm[Reward]], AlgorithmState), Arm[Reward]] =
    inspect {
      case (arms, state) =>
        arms(selectArm(arms, state))
    }

  def updateState(chosenArm:Arm[Reward], reward: Reward): SState[(Seq[Arm[Reward]], AlgorithmState), AlgorithmState] =
    inspect {
      case (arms, state) =>
        updateState(arms, state, arms.indexOf(chosenArm), reward)
    }
}

You may need to implement your own arm simulator. Arm is modeled by breeze.stats.distributions.Rand[+T]. Please refer to Arms.scala for typical arm implementations.

License

The MIT License (MIT)

Copyright (c) 2016 Shingo Omura

Contributions

Contributions are welcome :-) There are no complicated regulations. Feel free to open issues and pull requests!