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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 51–75 of 514 papers

TitleStatusHype
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte CarloCode1
Safe Guaranteed Exploration for Non-linear SystemsCode1
BeBold: Exploration Beyond the Boundary of Explored RegionsCode1
See, Hear, Explore: Curiosity via Audio-Visual AssociationCode1
Adversarially Guided Actor-CriticCode1
GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic EnvironmentsCode1
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?Code1
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the LoopCode1
Training a Generally Curious AgentCode1
Landmark-Guided Subgoal Generation in Hierarchical Reinforcement LearningCode1
Evolutionary Large Language Model for Automated Feature TransformationCode1
Hierarchical Skills for Efficient ExplorationCode1
Learning Math Reasoning from Self-Sampled Correct and Partially-Correct SolutionsCode1
A Survey of Label-Efficient Deep Learning for 3D Point CloudsCode1
Diffusion-Reinforcement Learning Hierarchical Motion Planning in Multi-agent Adversarial GamesCode1
Novelty Search in Representational Space for Sample Efficient ExplorationCode1
Exciting Action: Investigating Efficient Exploration for Learning Musculoskeletal Humanoid LocomotionCode1
A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning—0
BooVI: Provably Efficient Bootstrapped Value Iteration—0
Deep density networks and uncertainty in recommender systems—0
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration—0
Biased Estimates of Advantages over Path Ensembles—0
KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance—0
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning—0
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