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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 141–150 of 514 papers

TitleStatusHype
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
A Web-scale system for scientific knowledge exploration—0
Efficient Policy Space Response Oracles—0
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable—0
Context-Dependent Upper-Confidence Bounds for Directed Exploration—0
Active Model Estimation in Markov Decision Processes—0
Constrained Hybrid Metaheuristic Algorithm for Probabilistic Neural Networks Learning—0
AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation—0
ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization—0
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation—0
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