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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 201–225 of 514 papers

TitleStatusHype
Cognitive Planning for Object Goal Navigation using Generative AI Models—0
Deep density networks and uncertainty in recommender systems—0
GraphEQA: Using 3D Semantic Scene Graphs for Real-time Embodied Question Answering—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
Efficient Exploration of Image Classifier Failures with Bayesian Optimization and Text-to-Image Models—0
Feature Engineering for Predictive Modeling using Reinforcement Learning—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Joint channel estimation and data detection in massive MIMO systems based on diffusion models—0
Finding Waldo: Towards Efficient Exploration of NeRF Scene Spaces—0
Finedeep: Mitigating Sparse Activation in Dense LLMs via Multi-Layer Fine-Grained Experts—0
DEEPGONET: Multi-label Prediction of GO Annotation for Protein from Sequence Using Cascaded Convolutional and Recurrent Network—0
FIT-SLAM -- Fisher Information and Traversability estimation-based Active SLAM for exploration in 3D environments—0
A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints—0
Computing low-thrust transfers in the asteroid belt, a comparison between astrodynamical manipulations and a machine learning approach—0
Efficient Exploration of Gradient Space for Online Learning to Rank—0
Fractional Langevin Monte Carlo: Exploring Lévy Driven Stochastic Differential Equations for Markov Chain Monte Carlo—0
FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching—0
From Automation to Autonomy in Smart Manufacturing: A Bayesian Optimization Framework for Modeling Multi-Objective Experimentation and Sequential Decision Making—0
From proprioception to long-horizon planning in novel environments: A hierarchical RL model—0
GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning—0
Efficient Exploration in Resource-Restricted Reinforcement Learning—0
Better Exploration with Optimistic Actor-Critic—0
Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs—0
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