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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 1120 of 514 papers

TitleStatusHype
Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language NavigationCode2
Online Decision TransformerCode2
Contextualizing biological perturbation experiments through languageCode1
Training a Generally Curious AgentCode1
Maximum Entropy Reinforcement Learning with Diffusion PolicyCode1
GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic EnvironmentsCode1
Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic LearningCode1
Leveraging Skills from Unlabeled Prior Data for Efficient Online ExplorationCode1
Persistent Sampling: Enhancing the Efficiency of Sequential Monte CarloCode1
Exciting Action: Investigating Efficient Exploration for Learning Musculoskeletal Humanoid LocomotionCode1
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