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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 41–50 of 514 papers

TitleStatusHype
Hierarchical Skills for Efficient ExplorationCode1
Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* NeighborhoodCode1
A Langevin-like Sampler for Discrete DistributionsCode1
Adversarially Guided Actor-CriticCode1
BeBold: Exploration Beyond the Boundary of Explored RegionsCode1
Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic LearningCode1
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the LoopCode1
A Survey of Label-Efficient Deep Learning for 3D Point CloudsCode1
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?Code1
Contextualizing biological perturbation experiments through languageCode1
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