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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 161–170 of 514 papers

TitleStatusHype
TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly DetectionCode0
Joint channel estimation and data detection in massive MIMO systems based on diffusion models—0
Consensus-based adaptive sampling and approximation for high-dimensional energy landscapesCode0
Virtual Action Actor-Critic Framework for Exploration (Student Abstract)—0
Regret Analysis of Learning-Based Linear Quadratic Gaussian Control with Additive Exploration—0
Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning—0
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion—0
Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation Using Vision Language Models—0
Information Content Exploration—0
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