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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 81–90 of 514 papers

TitleStatusHype
Interpretable SHAP-bounded Bayesian Optimization for Underwater Acoustic Metamaterial Coating Design—0
An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm—0
Aerial Active STAR-RIS-assisted Satellite-Terrestrial Covert Communications—0
Lumos: Efficient Performance Modeling and Estimation for Large-scale LLM Training—0
Memetic Search for Green Vehicle Routing Problem with Private Capacitated Refueling Stations—0
From Automation to Autonomy in Smart Manufacturing: A Bayesian Optimization Framework for Modeling Multi-Objective Experimentation and Sequential Decision Making—0
Entropy-guided sequence weighting for efficient exploration in RL-based LLM fine-tuning—0
Maya: Optimizing Deep Learning Training Workloads using Emulated Virtual Accelerators—0
FALCONEye: Finding Answers and Localizing Content in ONE-hour-long videos with multi-modal LLMs—0
KEA: Keeping Exploration Alive by Proactively Coordinating Exploration Strategies—0
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