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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 391400 of 514 papers

TitleStatusHype
Few-shot_LLM_Synthetic_Data_with_Distribution_MatchingCode0
Learning to Score Behaviors for Guided Policy OptimizationCode0
Meta-Learning for Stochastic Gradient MCMCCode0
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based GamesCode0
Meta-Learning Integration in Hierarchical Reinforcement Learning for Advanced Task ComplexityCode0
Federated Control with Hierarchical Multi-Agent Deep Reinforcement LearningCode0
Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug DesignCode0
A diversity-enhanced genetic algorithm for efficient exploration of parameter spacesCode0
A Variational Approach to Bayesian Phylogenetic InferenceCode0
Count-Based Exploration with the Successor RepresentationCode0
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