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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 101110 of 514 papers

TitleStatusHype
Personalized Algorithmic Recourse with Preference ElicitationCode0
Bayesian Reinforcement Learning via Deep, Sparse SamplingCode0
Count-Based Exploration with the Successor RepresentationCode0
Deep Exploration via Bootstrapped DQNCode0
Count-Based Exploration in Feature Space for Reinforcement LearningCode0
Instance Temperature Knowledge DistillationCode0
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal BabblingCode0
A New Bandit Setting Balancing Information from State Evolution and Corrupted ContextCode0
Few-shot_LLM_Synthetic_Data_with_Distribution_MatchingCode0
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based GamesCode0
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