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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 91–100 of 514 papers

TitleStatusHype
KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance—0
Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks—0
Persistent Sampling: Enhancing the Efficiency of Sequential Monte CarloCode1
Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning—0
Online Learning for Autonomous Management of Intent-based 6G Networks—0
ParamsDrag: Interactive Parameter Space Exploration via Image-Space Dragging—0
Scalable Exploration via Ensemble++Code0
Exciting Action: Investigating Efficient Exploration for Learning Musculoskeletal Humanoid LocomotionCode1
Preference-Guided Reinforcement Learning for Efficient ExplorationCode0
Uncertainty-Guided Optimization on Large Language Model Search TreesCode0
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