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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

Showing 401410 of 514 papers

TitleStatusHype
Adaptive teachers for amortized samplersCode0
TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly DetectionCode0
ASCENT: Amplifying Power Side-Channel Resilience via Learning & Monte-Carlo Tree SearchCode0
Generalization and Exploration via Randomized Value FunctionsCode0
Personalized Algorithmic Recourse with Preference ElicitationCode0
Feature Interaction Aware Automated Data Representation TransformationCode0
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
GenPlan: Generative Sequence Models as Adaptive PlannersCode0
Exploring through Random Curiosity with General Value FunctionsCode0
Scalable Exploration via Ensemble++Code0
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