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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 131–140 of 514 papers

Title	Date	Tasks	Status	Score
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling	Jan 22, 2020	Decision MakingEfficient Exploration	CodeCode Available	5
Personalized Algorithmic Recourse with Preference Elicitation	May 27, 2022	Efficient Exploration	CodeCode Available	5
Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share?	Feb 24, 2022	Efficient ExplorationTransfer Learning	CodeCode Available	5
Generalization and Exploration via Randomized Value Functions	Feb 4, 2014	Efficient Explorationreinforcement-learning	CodeCode Available	5
GenPlan: Generative Sequence Models as Adaptive Planners	Dec 11, 2024	Decision MakingDenoising	CodeCode Available	5
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning	Sep 13, 2018	Autonomous VehiclesEfficient 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
Feature Interaction Aware Automated Data Representation Transformation	Sep 29, 2023	Automated Feature EngineeringDecision Making	CodeCode Available	5
Federated Control with Hierarchical Multi-Agent Deep Reinforcement Learning	Dec 22, 2017	Deep Reinforcement LearningEfficient Exploration	CodeCode Available	5
Dynamic Subgoal-based Exploration via Bayesian Optimization	Oct 21, 2019	Bayesian OptimizationEfficient Exploration	CodeCode Available	5

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