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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 151160 of 514 papers

TitleStatusHype
Personalized Algorithmic Recourse with Preference ElicitationCode0
Efficient Exploration of the Rashomon Set of Rule Set ModelsCode0
Concurrent Meta Reinforcement LearningCode0
Efficient Exploration through Bayesian Deep Q-NetworksCode0
Federated Control with Hierarchical Multi-Agent Deep Reinforcement LearningCode0
Feature Interaction Aware Automated Data Representation TransformationCode0
Few-shot_LLM_Synthetic_Data_with_Distribution_MatchingCode0
Lagrangian Manifold Monte Carlo on Monge PatchesCode0
Exploring through Random Curiosity with General Value FunctionsCode0
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
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