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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

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Showing 161–170 of 514 papers

Title	Date	Tasks	Status	Score
Few-shot_LLM_Synthetic_Data_with_Distribution_Matching	Feb 9, 2025	AttributeEfficient Exploration	CodeCode Available	5
Learning to Act with Affordance-Aware Multimodal Neural SLAM	Jan 24, 2022	Efficient ExplorationTest unseen	CodeCode Available	5
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games	May 1, 2022	Deep Reinforcement LearningEfficient Exploration	CodeCode Available	5
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning	Feb 12, 2018	Efficient Explorationreinforcement-learning	CodeCode Available	5
Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits	Feb 17, 2024	Computational EfficiencyEfficient Exploration	CodeCode Available	5
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables	Mar 19, 2019	Deep Reinforcement LearningEfficient Exploration	CodeCode Available	5
Exploring through Random Curiosity with General Value Functions	Nov 18, 2022	Efficient Exploration	CodeCode Available	5
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement Learning	May 30, 2023	Efficient ExplorationMulti-agent Reinforcement Learning	CodeCode Available	5
Dynamic Subgoal-based Exploration via Bayesian Optimization	Oct 21, 2019	Bayesian OptimizationEfficient Exploration	CodeCode Available	5
Exploratory State Representation Learning	Sep 28, 2021	DiversityEfficient Exploration	CodeCode Available	5

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