CURL: Contrastive Unsupervised Representations for Reinforcement Learning

Jul 12, 2020

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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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About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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