Reinforcement learning explained

For a deep dive into the current state of AI and where we might be headed in coming years, check out our free ebook "What is Artificial Intelligence," by Mike Loukides and Ben Lorica. A robot takes a big step forward, then falls. The next time, it takes a smaller step and is able to hold its balance. The robot tries variations like this many times; eventually, it learns the right size of steps to take and walks steadily. What we see here is called reinforcement learning. It directly connects a robot's action with an outcome, without the robot having to learn a complex relationship between its action and results. The robot learns how to walk based on reward (staying on balance) and punishment (falling).