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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 61–70 of 514 papers

TitleStatusHype
A Survey of Label-Efficient Deep Learning for 3D Point CloudsCode1
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep NetworksCode1
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?Code1
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the LoopCode1
Diffusion-Reinforcement Learning Hierarchical Motion Planning in Multi-agent Adversarial GamesCode1
Maximum Entropy Reinforcement Learning with Diffusion PolicyCode1
Adversarially Guided Actor-CriticCode1
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning—0
BooVI: Provably Efficient Bootstrapped Value Iteration—0
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