Papers › CURL: Contrastive Unsupervised Representation Learning for Reinforcement Learning
CURL: Contrastive Unsupervised Representation Learning for Reinforcement Learning
Michael Laskin, Pieter Abbeel, Aravind Srinivas
Reinforcement Learning for control tasks where the agent learns from raw high dimensional pixels has proven to be difficult and sample-inefficient. Operating on high-dimensional observational input poses a challenging credit assignment problem, which hinders the agent’s ability to learn optimal policies quickly. One promising approach to improve the sample efficiency of image-based RL algorithms is to learn low-dimensional representations from the raw input using unsupervised learning. To that end, we propose a new model: Contrastive Unsupervised Representation Learning for Reinforcement Learning (CURL). CURL extracts high level features from raw pixels using a contrastive learning objective and performs off-policy control on top of the extracted features. CURL achieves state-of-the-art performance and is the first image based algorithm across both model-free and model-based settings to nearly match the sample-efficiency and performance of state-based features on five out of the six DeepMind control benchmarks.
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