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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 241–250 of 514 papers

TitleStatusHype
Guided Exploration for Efficient Relational Model Learning—0
Hands-Free Segmentation of Medical Volumes via Binary Inputs—0
Feature Engineering for Predictive Modeling using Reinforcement Learning—0
Deep exploration by novelty-pursuit with maximum state entropy—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm—0
Adaptive Exploration for Multi-Reward Multi-Policy Evaluation—0
Feature and Instance Joint Selection: A Reinforcement Learning Perspective—0
Fast exploration and learning of latent graphs with aliased observations—0
FALCONEye: Finding Answers and Localizing Content in ONE-hour-long videos with multi-modal LLMs—0
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