thompson 1.1.0

The multi-armed bandit by Thompson Sampling, UCB-Upper confidence Bound, and randomized sampling.

Multi-armed bandit

• Thompson is Python package to evaluate the multi-armed bandit problem. In addition to thompson, Upper Confidence Bound (UCB) algorithm, and randomized results are also implemented. The thompson package implements three algorithms for solving the multi-armed bandit problem:

1. Thompson Sampling: A Bayesian approach that maintains probability distributions over the expected rewards of each arm and samples from these distributions to select the next arm to pull.

2. Upper Confidence Bound (UCB): A deterministic algorithm that selects arms based on their estimated rewards and the uncertainty in those estimates.

3. Randomized Sampling: A baseline method that randomly selects arms without considering their past performance.

The multi-armed bandit problem is a classic reinforcement learning problem that exemplifies the exploration-exploitation tradeoff dilemma. In this problem, a fixed limited set of resources must be allocated between competing choices in a way that maximizes expected gain, when each choice's properties are only partially known at the time of allocation.

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Install thompson from PyPI

pip install thompson

Import thompson package

import thompson as th

Documentation pages

On the documentation pages you can find detailed information about the working of the thompson with examples.

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Examples

• Example: Compute multi-armed bandit using Thompson

• Example: Compute multi-armed bandit using UCB-Upper confidence Bound

• Example: Compute multi-armed bandit using randomized data

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References

• https://en.wikipedia.org/wiki/Multi-armed_bandit