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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

Showing 1120 of 514 papers

TitleStatusHype
Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language NavigationCode2
Online Decision TransformerCode2
Generative Colorization of Structured Mobile Web PagesCode1
Evolutionary Large Language Model for Automated Feature TransformationCode1
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
Exciting Action: Investigating Efficient Exploration for Learning Musculoskeletal Humanoid LocomotionCode1
GeoThermalCloud: Machine Learning for Geothermal Resource ExplorationCode1
A Langevin-like Sampler for Discrete DistributionsCode1
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the LoopCode1
BeBold: Exploration Beyond the Boundary of Explored RegionsCode1
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