Lunar Landings from Demonstrations – Towards Data Science

Deep reinforcement learning algorithms have achieved remarkable results on a number of problems once thought to be unsolvable without the aid of human intuition and creativity. RL agents can learn to master tasks like chess and retro video games without any prior instruction -- often surpassing the performance of even the greatest human experts. But these methods are sample inefficient and rely on learning from hundreds or even thousands of complete failures before any progress is made. That's a luxury we can afford when the task is simple or can be simulated, like an Atari screen or chess board, but is at least partially responsible for RL's relatively short list of real-world applications. For example, it would be incredibly dangerous, expensive, and time inefficient to let a self-driving algorithm learn by smashing a real car into a real wall for the 1000 iterations it might take for it to figure out what the brakes do, or to learn to land a rocket by crashing the first 500 of them.