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Self-supervised Reinforcement Learning

Self-supervised Reinforcement Learning (SSL-RL) augments standard RL training with self-supervised auxiliary objectives — such as contrastive, predictive, or data-augmentation-based tasks — applied to the agent's own experience. These objectives improve the quality of learned representations without requiring extra human labels, enabling faster convergence and better sample efficiency, especially in high-dimensional observation spaces like raw pixels.

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Sources

  1. Laskin, M., Srinivas, A., & Abbeel, P. (2020). CURL: Contrastive Unsupervised Representations for Reinforcement Learning. Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119, 5639–5650. link ↗
  2. Laskin, M., Lee, K., Stooke, A., Pinto, L., Abbeel, P., & Srinivas, A. (2021). Reinforcement Learning with Augmented Data. Advances in Neural Information Processing Systems (NeurIPS), 33, 19884–19895. link ↗

Related methods

Reinforcement LearningSelf-supervised convolutional neural networkSemi-supervised Reinforcement LearningTransfer Learning with Reinforcement Learning

Referenced by

Fine-Tuned Reinforcement LearningMultimodal Reinforcement LearningSemi-supervised Reinforcement LearningWeakly supervised reinforcement learning

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ScholarGate — Self-supervised Reinforcement Learning (Self-supervised Reinforcement Learning (SSL-augmented RL)). Retrieved 2026-06-04 from https://scholargate.app/en/deep-learning/self-supervised-reinforcement-learning