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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 111120 of 514 papers

TitleStatusHype
Few-shot_LLM_Synthetic_Data_with_Distribution_MatchingCode0
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based GamesCode0
Personalized Algorithmic Recourse with Preference ElicitationCode0
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
A Variational Approach to Bayesian Phylogenetic InferenceCode0
Feature Interaction Aware Automated Data Representation TransformationCode0
A Gradient Sampling Algorithm for Stratified Maps with Applications to Topological Data AnalysisCode0
Consensus-based adaptive sampling and approximation for high-dimensional energy landscapesCode0
Exploring through Random Curiosity with General Value FunctionsCode0
Federated Control with Hierarchical Multi-Agent Deep Reinforcement LearningCode0
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