2 – Applications

The applications of reinforcement learning are numerous and diverse. Ranging from self-driving cars to board games. For instance, one of the major breakthroughs in machine learning in the 90s was TD-Gammon. An algorithm that used RL to play Backgammon on par with the best Backgammon players at the time. This algorithm advanced the theory of Backgammon by discovering strategies that were previously unknown. You know, Backgammon is a pretty complicated game. In fact, it has over 10 to the 20th possible game states. So it’s pretty amazing that it was possible to teach an artificially intelligent agent how to play. More recently, progress was made on a game that is much more complicated. Maybe you’ve heard of AlphaGo. An AI agent trained to beat professional Go players. It’s said that there are more configurations in the game than there are atoms in the universe. RL is also used to play video games such as Atari Breakout. The AI agent is given no prior knowledge of what a ball is or what the controls do. It only sees the screen and its score. Then through interacting with the game, with testing out the various controls, it’s able to devise a strategy to maximize its score. RL was also used to create a bot to beat top players in the online battle arena video game Dota. If you are an e-sports fan, you’re encouraged to check it out. Jumping to a completely different domain, RL is also used in robotics. For instance, it’s been used to teach robots to walk. The idea is that we can give the robot time to test out its new legs to see what works and what doesn’t work for staying upright. Then we can create an algorithm to help it learn from that gained experience, so it’s able to walk like a pro. But why teach a robot to walk when you can teach it to drive? RL is used successfully in self-driving cars, ships, and airplanes. It’s even been used in finance, biology, telecommunication, and inventory management among other things. If any of these applications catch your interest, check out the links below.

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