Practical Deep Learning for Cloud, Mobile, and Edge

Chapter 17. Building an Autonomous Car in Under an Hour: Reinforcement Learning with AWS DeepRacer

Contributed by guest author: Sunil Mallya

If you follow technology news, you probably have seen a resurgence in debates about when computers are going to take over the world. Although that’s a fun thought exercise, what’s triggered the resurgence in these debates? A large part of the credit can be attributed to the news of computers beating humans at decision-making tasks—winning in chess, achieving high scores in video games like Atari (2013), beating humans in a complex game of Go (2016), and, finally, beating human teams at Defence of the Ancients (Dota) 2 in 2017. The most astonishing thing about these successes is that the “bots” learned the games by playing against one another and reinforcing the strategies that they found to bring them success.

If we think more broadly on this concept, it’s no different than how humans teach their pets. To train a dog, every good behavior is reinforced by rewarding the dog with a treat and lots of hugs, and every undesired behavior is discouraged by asserting “bad doggie.” This concept of reinforcing good behaviors and discouraging bad ones essentially forms the crux of reinforcement learning.

Computer games, or games in general, require a sequence of decisions to be made, so traditional supervised methods aren’t well suited, because they often focus on making a single decision (e.g., is this an image of a cat or a dog?). The inside joke in ...