What is wrong with reinforcement learning? (Ep. 82)

Join the discussion on our Discord server After reinforcement learning agents doing great at playing Atari video games, Alpha Go, doing financial trading, dealing with language modeling, let me tell you the real story here.In this episode I want to shine some light on reinforcement learning (RL) and the limitations that every practitioner should consider before taking certain directions. RL seems to work so well! What is wrong with it? Are you a listener of Data Science at Home podcast? A reader of the Amethix Blog? Or did you subscribe to the Artificial Intelligence at your fingertips newsletter? In any case let’s stay in touch! https://amethix.com/survey/  ReferencesEmergence of Locomotion Behaviours in Rich Environments https://arxiv.org/abs/1707.02286Rainbow: Combining Improvements in Deep Reinforcement Learning https://arxiv.org/abs/1710.02298AlphaGo Zero: Starting from scratch https://deepmind.com/blog/article/alphago-zero-starting-scratch This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceathome.substack.com