CURL: Contrastive Unsupervised Representations for Reinforcement Learning

This video explains a new algorithm from UC Berkeley that adds the Momentum Contrastive Learning framework to Reinforcement Learning. This loss improves the mapping from raw observations into latent spaces for control. CURL achieves sample-efficiency and performance gains on the DeepMind Control Suite only from pixel inputs, without the need for physical state inputs! Thanks for watching! Please Subscribe! Paper Links: CURL: https://ift.tt/2UT4PyG Intro Video on the DeepMind Control Suite: https://www.youtube.com/watch?v=rAai4QzcYbs MoCo: https://ift.tt/2qdsi1c MoCo v2: https://ift.tt/2xtZ81r RoboNet: https://ift.tt/2N8KikK World Models: https://ift.tt/2E06KWF MuZero: https://ift.tt/37n6SiK PlaNet: https://ift.tt/2A58PRm Dreamer: https://ift.tt/2qiWpUW