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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 201–225 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
Guarded Policy Optimization with Imperfect Online Demonstrations—0
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization—0
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation—0
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret—0
Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical RobotCode2
Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization—0
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning—0
Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs—0
GFlowNets for AI-Driven Scientific Discovery—0
ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation—0
Embodied Agents for Efficient Exploration and Smart Scene Description—0
Exploration in Model-based Reinforcement Learning with Randomized Reward—0
Strangeness-driven Exploration in Multi-Agent Reinforcement LearningCode0
SHIRO: Soft Hierarchical Reinforcement Learning—0
Generative Colorization of Structured Mobile Web PagesCode1
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