• 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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pip install thompson

import thompson as th

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

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