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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

Showing 201210 of 514 papers

TitleStatusHype
Go-Explore for Residential Energy Management0
Mutual Enhancement of Large Language and Reinforcement Learning Models through Bi-Directional Feedback Mechanisms: A Case Study0
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous and Instruction-guided Driving0
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration0
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 models0
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 Exploration0
Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning0
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