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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 3140 of 514 papers

TitleStatusHype
HyperDQN: A Randomized Exploration Method for Deep Reinforcement LearningCode1
Evolutionary Large Language Model for Automated Feature TransformationCode1
Diffusion-Reinforcement Learning Hierarchical Motion Planning in Multi-agent Adversarial GamesCode1
Exciting Action: Investigating Efficient Exploration for Learning Musculoskeletal Humanoid LocomotionCode1
Contextualizing biological perturbation experiments through languageCode1
Adversarially Guided Actor-CriticCode1
Automatic chemical design using a data-driven continuous representation of moleculesCode1
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep NetworksCode1
A Survey of Label-Efficient Deep Learning for 3D Point CloudsCode1
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