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Found 3 papers, 3 papers with code
Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning
From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view.
Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control
Deep networks have enabled reinforcement learning to scale to more complex and challenging domains, but these methods typically require large quantities of training data.
Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means
Recently, neuro-inspired episodic control (EC) methods have been developed to overcome the data-inefficiency of standard deep reinforcement learning approaches.
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