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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 171–180 of 514 papers

Title	Date	Tasks	Status	Score
Feature Interaction Aware Automated Data Representation Transformation	Sep 29, 2023	Automated Feature EngineeringDecision Making	CodeCode Available	5
Meta-Learning for Stochastic Gradient MCMC	Jun 12, 2018	Efficient ExplorationMeta-Learning	CodeCode Available	5
Exploratory State Representation Learning	Sep 28, 2021	DiversityEfficient Exploration	CodeCode Available	5
Exploring through Random Curiosity with General Value Functions	Nov 18, 2022	Efficient Exploration	CodeCode Available	5
Federated Control with Hierarchical Multi-Agent Deep Reinforcement Learning	Dec 22, 2017	Deep Reinforcement LearningEfficient Exploration	CodeCode Available	5
A diversity-enhanced genetic algorithm for efficient exploration of parameter spaces	Dec 22, 2024	DiversityEfficient Exploration	CodeCode Available	5
Count-Based Exploration with the Successor Representation	Jul 31, 2018	Atari GamesEfficient Exploration	CodeCode Available	5
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes	Jul 6, 2018	Efficient Exploration	CodeCode Available	5
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration	Jun 2, 2020	DiversityEfficient Exploration	CodeCode Available	5
Estimating Risk and Uncertainty in Deep Reinforcement Learning	May 23, 2019	Bayesian InferenceDeep Reinforcement Learning	CodeCode Available	5

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