Model-based reinforcement learning with neural network dynamics

Enabling robots to act autonomously in the real-world is difficult. Even with expensive robots and teams of world-class researchers, robots still have difficulty autonomously navigating and interacting in complex, unstructured environments. A learned neural network dynamics model enables a hexapod robot to learn to run and follow desired trajectories, using just 17 minutes of real-world experience. Why are autonomous robots not out in the world among us? Engineering systems that can cope with all the complexities of our world is hard.