Momentum Predictive Representations Explained!

This video explains Momentum Predictive Representations, the latest advancement in Data-Efficient Reinforcement Learning by using an auxiliary contrastive self-supervised learning loss. This is a very interesting setup of the contrastive learning problem with temporal consistency rather than comparing augmented views of the same image alone. Thanks for watching! Please Subscribe! Paper Links: Momentum Predictive Representations: https://ift.tt/2Ei9qF1 Momentum Contrastive Learning: https://ift.tt/2xtZ81r CURL: https://ift.tt/3dLGpxF Bootstrap your own Latent: https://ift.tt/38m1mhx Can RL from Pixels be as Efficient as RL from State? https://ift.tt/3eKC8uu MuZero: https://ift.tt/37lLv1o Offline RL survey: https://ift.tt/2ZVnqg3 ICML 2020 Model-Based RL: https://ift.tt/3fGKp44 RL with Augmented Data: https://ift.tt/2YqpgFo Chapters 0:00 Beginning 0:13 Quick Overview 2:52 Data-Efficient Deep RL 3:48 Directions to Data-Efficient RL 5:58 Momentum Contrastive Learning (MoCo) 7:18 Bootstrap your own Latent (Power of Contrast w/ Online and Target Network) 8:14 CURL, using this in RL 9:08 Temporal Contrastive Loss 10:02 Algorithm Pseudocode Walkthrough 12:52 Ablations 13:20 MPR for Monte Carlo Tree Search as in MuZero - Future Work