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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 2130 of 514 papers

TitleStatusHype
A Langevin-like Sampler for Discrete DistributionsCode1
Evolutionary Large Language Model for Automated Feature TransformationCode1
Diffusion-Reinforcement Learning Hierarchical Motion Planning in Multi-agent Adversarial GamesCode1
Generative Colorization of Structured Mobile Web PagesCode1
GeoThermalCloud: Machine Learning for Geothermal Resource ExplorationCode1
GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic EnvironmentsCode1
A Survey of Label-Efficient Deep Learning for 3D Point CloudsCode1
SC-Explorer: Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and PlanningCode1
Landmark-Guided Subgoal Generation in Hierarchical Reinforcement LearningCode1
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
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