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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 221–230 of 514 papers

TitleStatusHype
Embodied Agents for Efficient Exploration and Smart Scene Description—0
Exploration in Model-based Reinforcement Learning with Randomized Reward—0
Strangeness-driven Exploration in Multi-Agent Reinforcement LearningCode0
SHIRO: Soft Hierarchical Reinforcement Learning—0
Generative Colorization of Structured Mobile Web PagesCode1
Reinforcement Learning in Credit Scoring and Underwriting—0
Efficient Exploration in Resource-Restricted Reinforcement Learning—0
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning—0
CURO: Curriculum Learning for Relative Overgeneralization—0
Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions—0
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