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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
Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical RobotCode2
MermaidFlow: Redefining Agentic Workflow Generation via Safety-Constrained Evolutionary ProgrammingCode2
Generative Colorization of Structured Mobile Web PagesCode1
Evolutionary Large Language Model for Automated Feature TransformationCode1
BeBold: Exploration Beyond the Boundary of Explored RegionsCode1
Exciting Action: Investigating Efficient Exploration for Learning Musculoskeletal Humanoid LocomotionCode1
GeoThermalCloud: Machine Learning for Geothermal Resource ExplorationCode1
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?Code1
A Survey of Label-Efficient Deep Learning for 3D Point CloudsCode1
Diffusion-Reinforcement Learning Hierarchical Motion Planning in Multi-agent Adversarial GamesCode1
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