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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 51–60 of 514 papers

TitleStatusHype
Maximum Entropy Reinforcement Learning with Diffusion PolicyCode1
A Survey of Label-Efficient Deep Learning for 3D Point CloudsCode1
BeBold: Exploration Beyond the Boundary of Explored RegionsCode1
Model-Based Active ExplorationCode1
Occupancy Anticipation for Efficient Exploration and NavigationCode1
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?Code1
Improving Protein Optimization with Smoothed Fitness LandscapesCode1
Paradiseo: From a Modular Framework for Evolutionary Computation to the Automated Design of Metaheuristics ---22 Years of Paradiseo---Code1
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte CarloCode1
Contextualizing biological perturbation experiments through languageCode1
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