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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 341–350 of 514 papers

TitleStatusHype
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
GFlowNets for AI-Driven Scientific Discovery—0
GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices—0
Goal-oriented Trajectories for Efficient Exploration—0
Go-Browse: Training Web Agents with Structured Exploration—0
Go-Explore for Residential Energy Management—0
GraphEQA: Using 3D Semantic Scene Graphs for Real-time Embodied Question Answering—0
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