Exploring the Multi-Armed Bandit Problem with Python: A Simple Reinforcement Learning Example
Reinforcement learning (RL) is a powerful branch of machine learning that focuses on how agents should take actions in an environment to maximize cumulative rewards. One of the simplest and most famous problems in RL is the Multi-Armed Bandit problem, which illustrates the fundamental challenge of balancing exploration (trying new things) with exploitation (sticking to known actions that provide the best results).
In this post, we’ll explore the Multi-Armed Bandit problem in detail, understand key concepts like the epsilon-greedy strategy, and implement a basic Python solution to see how an agent can learn to choose the best option.
The code for this post is available at this GitHub page.
Key Concepts in Reinforcement Learning
Before diving into the Multi-Armed Bandit problem, let’s outline a few important concepts in reinforcement learning:
- Agent: The decision-maker, which in this case is the player pulling the slot machine levers.
- Actions: Different slot machines (or levers) that the agent can pull. Each has an unknown probability of winning.
- Rewards: The outcome after pulling a lever — either a win (1) or a…
