Optimizing Exploration in Reinforcement Learning: (UCB) Strategy for Multi-Armed Bandit Ch 5 

In this part of the series, we explore the Upper Confidence Bound (UCB) Approach, a highly effective strategy in Reinforcement Learning for tackling the Multi-Armed Bandit problem. I demonstrate how UCB helps balance exploration and exploitation by selecting arms based on both their estimated rewards and a confidence interval that narrows with more data.
In this Python implementation, we’ll see how the UCB algorithm dynamically adjusts its decision-making process by incorporating uncertainty bonuses into each arm's value. As the agent interacts with the environment, UCB updates reward estimates and optimizes choices based on previous actions and outcomes. If you’re interested in a mathematically grounded yet practical strategy for improving RL performance, this is a must-watch!