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Efficient Exploration

Efficient Exploration is one of the main obstacles in scaling up modern deep reinforcement learning algorithms. The main challenge in Efficient Exploration is the balance between exploiting current estimates, and gaining information about poorly understood states and actions.

Source: Randomized Value Functions via Multiplicative Normalizing Flows

Papers

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TitleStatusHype
Latent Action Priors for Locomotion with Deep Reinforcement Learning—0
Learn2Hop: Learned Optimization on Rough Landscapes—0
Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks—0
Learning Causal Overhypotheses through Exploration in Children and Computational Models—0
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