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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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Showing 501–514 of 514 papers

TitleStatusHype
Intrinsically Guided Exploration in Meta Reinforcement Learning—0
Intrinsic Rewards for Exploration without Harm from Observational Noise: A Simulation Study Based on the Free Energy Principle—0
Is a Good Foundation Necessary for Efficient Reinforcement Learning? The Computational Role of the Base Model in Exploration—0
Joint channel estimation and data detection in massive MIMO systems based on diffusion models—0
Joint Falsification and Fidelity Settings Optimization for Validation of Safety-Critical Systems: A Theoretical Analysis—0
JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning—0
KEA: Keeping Exploration Alive by Proactively Coordinating Exploration Strategies—0
K-Means Clustering using Tabu Search with Quantized Means—0
Language Agents Mirror Human Causal Reasoning Biases. How Can We Help Them Think Like Scientists?—0
Large-scale signatures of unconsciousness are consistent with a departure from critical dynamics—0
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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