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CURL: Contrastive Unsupervised Representations for Reinforcement Learning

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arxiv 2004.04136 v4 pith:QZLGNTTT submitted 2020-04-08 cs.LG cs.CVstat.ML

Aravind Srinivas , Michael Laskin , Pieter Abbeel This is my paper · ORCID
classification cs.LGcs.CVstat.ML
keywords curlcontrastivecontrolfeatureslearningdeepmindmethodsreinforcement
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We present CURL: Contrastive Unsupervised Representations for Reinforcement Learning. CURL extracts high-level features from raw pixels using contrastive learning and performs off-policy control on top of the extracted features. CURL outperforms prior pixel-based methods, both model-based and model-free, on complex tasks in the DeepMind Control Suite and Atari Games showing 1.9x and 1.2x performance gains at the 100K environment and interaction steps benchmarks respectively. On the DeepMind Control Suite, CURL is the first image-based algorithm to nearly match the sample-efficiency of methods that use state-based features. Our code is open-sourced and available at https://github.com/MishaLaskin/curl.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Self-Predictive Dynamics for Generalization of Vision-based Reinforcement Learning

    cs.CV 2025-06 conditional novelty 7.0 of 10

    A self-supervised auxiliary loss combining weak and strong augmentations, an adversarial discriminator, and inverse-then-forward latent dynamics improves both data efficiency and zero-shot generalization in vision-based RL.

  2. Self-supervised learning method using multiple sampling strategies for general-purpose audio representation

    cs.SD 2025-05 conditional novelty 6.0 of 10

    A multi-loss self-supervised audio model that adds frame-level contrast and pitch-shift prediction to the COLA clip-level loss improves downstream audio classification, event detection, and pitch detection.

  3. rQdia: Regularizing Q-Value Distributions With Image Augmentation

    cs.LG 2025-06 reject novelty 5.0 of 10

    rQdia regularizes Q-value distributions across augmented images, reporting improvements on continuous control and Atari benchmarks over DrQ, SAC, and Data-Efficient Rainbow.

  4. A Survey of State Representation Learning for Deep Reinforcement Learning

    cs.LG 2025-06 conditional novelty 4.0 of 10

    A six-class taxonomy of state representation learning methods for model-free online deep reinforcement learning, with selection guidelines, evaluation metrics, and future directions.

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