Introduction to Reinforcement Learning: Multi-Armed Bandit Simulation in Python

Reinforcement Learning (RL) is a machine learning approach where an agent learns optimal behavior by interacting with an environment through trial and error. The agent receives feedback in the form of rewards or penalties, aiming to maximize its cumulative reward over time. A core challenge in RL is the trade-off between exploration, trying new actions to discover their value, and exploitation, using actions already known to be effective. The formulation of an RL problem involves key components like the agent, environment, state, policy, reward signal, and value function. The ultimate goal is for the agent to learn a policy that maximizes long-term rewards rather than focusing on immediate gains.