Efficient Zero: Mastering Atari Games with Limited Data ( Machine Learning Research Paper Explained)

, efficientzero, muzero, atari Reinforcement Learning methods are notoriously datahungry. Notably, MuZero learns a latent world model just from scalar feedback of reward and policypredictions, and therefore relies on scale to perform well. However, most RL algorithms fail when presented with very little data. EfficientZero makes several improvements over MuZero that allows it to learn from astonishingly small amounts of data and outperform other methods by a large margin in the lowsample setting. This could be a staple algorithm for future RL research. OUTLINE: 0:00 Intro Outline 2:30 MuZero Recap 10:50 EfficientZero improvements 14:15 SelfSupervised consistency loss 17:50 Endtoend prediction of the value prefix 20:40 Modelbased offpolicy correction 25:45 Experimental Results Conclusion Paper: Code: Links: TabNine Code Completion (Referral): YouTube: