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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 371–380 of 514 papers

TitleStatusHype
Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs—0
Computing low-thrust transfers in the asteroid belt, a comparison between astrodynamical manipulations and a machine learning approach—0
Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation Using Vision Language Models—0
Constrained Hybrid Metaheuristic Algorithm for Probabilistic Neural Networks Learning—0
Context-Dependent Upper-Confidence Bounds for Directed Exploration—0
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable—0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
Bridging Text and Crystal Structures: Literature-driven Contrastive Learning for Materials Science—0
Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning—0
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning—0
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