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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 201–210 of 514 papers

TitleStatusHype
Cognitive Planning for Object Goal Navigation using Generative AI Models—0
Deep density networks and uncertainty in recommender systems—0
Fractional Langevin Monte Carlo: Exploring Lévy Driven Stochastic Differential Equations for Markov Chain Monte Carlo—0
FALCONEye: Finding Answers and Localizing Content in ONE-hour-long videos with multi-modal LLMs—0
Fast exploration and learning of latent graphs with aliased observations—0
Feature and Instance Joint Selection: A Reinforcement Learning Perspective—0
From proprioception to long-horizon planning in novel environments: A hierarchical RL model—0
Feature Engineering for Predictive Modeling using Reinforcement Learning—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Efficient Exploration of Image Classifier Failures with Bayesian Optimization and Text-to-Image Models—0
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