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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 491–500 of 514 papers

TitleStatusHype
Deep Exploration via Randomized Value Functions—0
Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted IlluminationCode0
Efficient Pose and Cell Segmentation using Column Generation—0
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable—0
Automatic chemical design using a data-driven continuous representation of moleculesCode1
Hands-Free Segmentation of Medical Volumes via Binary Inputs—0
Processing Document Collections to Automatically Extract Linked Data: Semantic Storytelling Technologies for Smart Curation Workflows—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Deep Exploration via Bootstrapped DQNCode0
Angrier Birds: Bayesian reinforcement learningCode0
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