Games are a perfect testbed for RL because they are created in order to challenge human capabilities, and, to complete them, skills common to the human brain are required (such as memory, reasoning, and coordination). Consequently, a computer that can play on the same level or better than a human must possess the same qualities. Moreover, games are easy to reproduce and can be easily simulated in computers. Video games proved to be very difficult to solve because of their partial observability (that is, only a fraction of the game is visible) and their huge search space (that is, it's impossible for a computer to simulate all possible configurations).
A breakthrough in games occurred when, in 2015, AlphaGo beat Lee Sedol in the ancient game of Go. This win occurred in spite of the prediction that it wouldn't. At the time, it was thought that no computer would be able to beat an expert in Go for the next 10 years. AlphaGo used both RL and supervised learning to learn from professional human games. A few years after that match, the next version, named AlphaGo Zero, beat AlphaGo 100 games to 0. AlphaGo Zero learned to play Go in only three days through self-play.
To capture the messiness and continuous nature of the real world, a team of five neural networks named OpenAI Five was trained to play DOTA 2, a real-time strategy game with two teams (each with five players) playing against each other. The steep learning curve in playing this game is due to the long time horizons (a game lasts for 45 minutes on average with thousands of actions), the partial observability (each player can only see a small area around themselves), and the high-dimensional continuous action and observation space. In 2018, OpenAI Five played against the top DOTA 2 players at The International, losing the match but showing innate capabilities in both collaboration and strategy skills. Finally, on April 13, 2019, OpenAI Five officially defeated the world champions in the game, becoming the first AI to beat professional teams in an esports game.