This GitHub repository serves as a comprehensive resource that houses the Python implementation of the epsilon-greedy action value method. The purpose of this implementation is to provide a solution to the challenging and widely studied problem known as the multi-armed bandit problem.
The multi-armed bandit problem refers to a scenario where an agent is faced with a set of slot machines, often referred to as "one-armed bandits," each with its unknown probability distribution of payouts. The agent's objective is to maximize its cumulative reward over a series of trials by selecting the most rewarding slot machine.
The epsilon-greedy action value method is a popular algorithmic approach used to address the multi-armed bandit problem. It balances the exploration of potentially lucrative but unexplored arms (slot machines) and the exploitation of arms that have shown promising results so far. The algorithm achieves this balance by assigning a parameter, epsilon, which determines the probability of exploring a new arm versus exploiting the currently estimated best arm.