Finding the gap in reinforcement learning. Deep reinforcement learning, the algorithm used by state-of-the-art game-playing bots, starts by providing an agent with a set of possible actions in the ...

A reinforcement-learning algorithm would thus have the system play lots of chess games (think potentially billions of them), enabling it to learn the best potential moves in a wide array of states ...

The results show that reinforcement learning can do more than master board games. When trained to solve long-standing puzzles in protein science, the software excelled at creating useful molecules.

So, reinforcement learning algorithms have all the same philosophical limitations as regular machine learning algorithms. These are already well-known by machine learning scientists.

Still, it’s fun to see these games becoming accessible to reinforcement learning. You can even dream of parlaying your video-game obsession into one of the hottest jobs going—AI researcher.

Reinforcement learning was part of the algorithms that were integral to achieving breakthrough results with chess, protein folding and Atari games. Likewise, OpenAI trained deep reinforcement ...

Developed by DeepMind, Agent57 uses the same deep reinforcement learning algorithm to achieve superhuman levels of play even in games that previous AIs have struggled with.

Reinforcement learning (RL) is a powerful type of artificial intelligence technology that can be used to learn strategies to optimally control large, complex systems such as manufacturing plants ...