NIPS 2017 -- Day 3 Highlights – Insight Data

Pieter started his invited talk by summarizing some of the key differences between supervised learning and Reinforcement Learning (RL). In essence, RL is mainly concerned with learning an effective policy to have an agent interact with the world in a way that best achieves a goal. For example, learning a policy on how to walk. Recently, RL has seen many success stories, such as learning to play Atari games from the raw pixel inputs, mastering the game of Go to a superhuman level, or effectively teaching simulated characters how to walk from scratch. However, one big gap between RL algorithms and humans, remains the time it takes to acquire new and effective policies.