In this part of the series, we focus on one of the key strategies in Reinforcement Learning—Random Exploration—within the context of the Multi-Armed Bandit problem. I demonstrate how a reinforcement learning agent can explore different options (or arms) randomly, gathering rewards and learning from each interaction.
We implement a random exploration algorithm in Python, simulating multiple iterations where each arm is selected at random. The reward is then computed based on the environment’s probabilities, and we calculate the average reward for each arm based on the number of visits. This simple yet effective approach sets the foundation for more sophisticated RL strategies in future episodes.
If you're looking to understand how randomness plays a role in reinforcement learning and want to see it in action, this video is for you!