Reinforcement Learning: An Evolution from Games to Real-World Impact – Day 78

Reinforcement Learning: An Evolution from Games to Real-World Impact Reinforcement Learning: An Evolution from Games to Real-World Impact Reinforcement Learning (RL) is a fascinating branch of machine learning, with its roots stretching back to the 1950s. Although not always in the limelight, RL made a significant impact in various domains, especially in gaming and machine control. In 2013, a team from DeepMind, a British startup, built a system capable of learning and excelling at Atari games using only raw pixels as input—without any knowledge of the game’s rules. This breakthrough led to DeepMind’s famous system, AlphaGo, defeating world Go champions and ignited a global interest in RL. The Foundations of Reinforcement Learning: How It Works In RL, an agent interacts with an environment, observes outcomes, and receives feedback through rewards. The agent’s objective is to maximize cumulative rewards over time, learning the best actions through trial and error. Term Explanation Agent The software or system making decisions. Environment The external setting with which the agent interacts. Reward Feedback from the environment based on the agent’s actions. Examples of RL Applications Here are a few tasks RL is well-suited for: Application Agent Environment Reward Robot Control Robot control program Real-world physical…