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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 181–190 of 514 papers

Title	Date	Tasks	Status	Score
Estimating Risk and Uncertainty in Deep Reinforcement Learning	May 23, 2019	Bayesian InferenceDeep Reinforcement Learning	CodeCode Available	5
Exploratory State Representation Learning	Sep 28, 2021	DiversityEfficient Exploration	CodeCode Available	5
A diversity-enhanced genetic algorithm for efficient exploration of parameter spaces	Dec 22, 2024	DiversityEfficient Exploration	CodeCode Available	5
Exploring through Random Curiosity with General Value Functions	Nov 18, 2022	Efficient Exploration	CodeCode Available	5
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration	Jun 2, 2020	DiversityEfficient Exploration	CodeCode Available	5
Dynamic Subgoal-based Exploration via Bayesian Optimization	Oct 21, 2019	Bayesian OptimizationEfficient 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
On Machine Learning-Driven Surrogates for Sound Transmission Loss Simulations	Apr 25, 2022	BIG-bench Machine LearningDecision Making	CodeCode Available	5
Generalization and Exploration via Randomized Value Functions	Feb 4, 2014	Efficient Explorationreinforcement-learning	CodeCode Available	5
Variance Networks: When Expectation Does Not Meet Your Expectations	Mar 10, 2018	Efficient ExplorationReinforcement Learning	CodeCode Available	5

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