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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

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Showing 141–150 of 514 papers

TitleStatusHype
GenNBV: Generalizable Next-Best-View Policy for Active 3D ReconstructionCode2
ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization—0
Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank BanditsCode0
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian OptimizationCode0
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction FollowingCode2
Iterated Denoising Energy Matching for Sampling from Boltzmann DensitiesCode2
Safe Guaranteed Exploration for Non-linear SystemsCode1
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?Code1
LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and CosmologyCode2
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