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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 201–210 of 514 papers

TitleStatusHype
Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution—0
Successor-Predecessor Intrinsic Exploration—0
Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models—0
Joint Falsification and Fidelity Settings Optimization for Validation of Safety-Critical Systems: A Theoretical Analysis—0
Rescue Conversations from Dead-ends: Efficient Exploration for Task-oriented Dialogue Policy Optimization—0
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement LearningCode0
Fast exploration and learning of latent graphs with aliased observations—0
Exploration of the search space of Gaussian graphical models for paired data—0
Policy Mirror Descent Inherently Explores Action Space—0
Exploration via Epistemic Value Estimation—0
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