What Is Reinforcement Learning?

Deep neural networks trained with reinforcement learning can encode complex behaviors. This allows an alternative approach to applications that are otherwise intractable or more challenging to tackle with more traditional methods. For example, in autonomous driving, a neural network can replace the driver and decide how to turn the steering wheel by simultaneously looking at multiple sensors such as camera frames and lidar measurements. Without neural networks, the problem would normally be broken down in smaller pieces like extracting features from camera frames, filtering the lidar measurements, fusing the sensor outputs, and making "driving" decisions based on sensor inputs. While reinforcement learning as an approach is still under evaluation for production systems, some industrial applications are good candidates for this technology.