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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 110 of 514 papers

TitleStatusHype
Cradle: Empowering Foundation Agents Towards General Computer ControlCode7
Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization ApproachCode7
LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical ReasoningCode5
Online Decision TransformerCode2
LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and CosmologyCode2
MermaidFlow: Redefining Agentic Workflow Generation via Safety-Constrained Evolutionary ProgrammingCode2
Iterated Denoising Energy Matching for Sampling from Boltzmann DensitiesCode2
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction FollowingCode2
GenNBV: Generalizable Next-Best-View Policy for Active 3D ReconstructionCode2
Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical RobotCode2
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