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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 281290 of 514 papers

TitleStatusHype
NovelD: A Simple yet Effective Exploration CriterionCode1
HelixMO: Sample-Efficient Molecular Optimization in Scene-Sensitive Latent Space0
IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions0
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement LearningCode0
Discovering and Exploiting Sparse Rewards in a Learned Behavior SpaceCode0
Bayesian optimization of distributed neurodynamical controller models for spatial navigation0
Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives0
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and GeneralizationCode0
Landmark-Guided Subgoal Generation in Hierarchical Reinforcement LearningCode1
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