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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 81–90 of 514 papers

Title	Date	Tasks	Status	Score
Behavior-Guided Actor-Critic: Improving Exploration via Learning Policy Behavior Representation for Deep Reinforcement Learning	Apr 9, 2021	Deep Reinforcement LearningEfficient Exploration	CodeCode Available	5
Angrier Birds: Bayesian reinforcement learning	Jan 6, 2016	Efficient ExplorationQ-Learning	CodeCode Available	5
GenPlan: Generative Sequence Models as Adaptive Planners	Dec 11, 2024	Decision MakingDenoising	CodeCode Available	5
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling	Jan 22, 2020	Decision MakingEfficient Exploration	CodeCode Available	5
Generalization and Exploration via Randomized Value Functions	Feb 4, 2014	Efficient Explorationreinforcement-learning	CodeCode Available	5
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement Learning	Sep 8, 2022	Efficient Explorationreinforcement-learning	CodeCode Available	5
Personalized Algorithmic Recourse with Preference Elicitation	May 27, 2022	Efficient Exploration	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
Bayesian Curiosity for Efficient Exploration in Reinforcement Learning	Nov 20, 2019	Efficient Explorationreinforcement-learning	CodeCode Available	5
Impact Makes a Sound and Sound Makes an Impact: Sound Guides Representations and Explorations	Aug 4, 2022	Efficient ExplorationUnsupervised Reinforcement Learning	CodeCode Available	5

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