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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 91100 of 514 papers

TitleStatusHype
Impact Makes a Sound and Sound Makes an Impact: Sound Guides Representations and ExplorationsCode0
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement LearningCode0
Amortized Variational Deep Q NetworkCode0
Generalization and Exploration via Randomized Value FunctionsCode0
Batch Bayesian Optimization via Local PenalizationCode0
Curiosity Driven Exploration of Learned Disentangled Goal SpacesCode0
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal BabblingCode0
Personalized Algorithmic Recourse with Preference ElicitationCode0
Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug DesignCode0
Balancing Value Underestimation and Overestimation with Realistic Actor-CriticCode0
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