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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 3140 of 514 papers

TitleStatusHype
A Survey of Label-Efficient Deep Learning for 3D Point CloudsCode1
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte CarloCode1
Generative Colorization of Structured Mobile Web PagesCode1
GeoThermalCloud: Machine Learning for Geothermal Resource ExplorationCode1
Learning Dexterous Manipulation from Exemplar Object Trajectories and Pre-GraspsCode1
SC-Explorer: Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and PlanningCode1
A Langevin-like Sampler for Discrete DistributionsCode1
Learning Math Reasoning from Self-Sampled Correct and Partially-Correct SolutionsCode1
Learning to Solve Combinatorial Graph Partitioning Problems via Efficient ExplorationCode1
The Sufficiency of Off-Policyness and Soft Clipping: PPO is still Insufficient according to an Off-Policy MeasureCode1
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