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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 51–100 of 514 papers

TitleStatusHype
Adversarially Guided Actor-CriticCode1
BeBold: Exploration Beyond the Boundary of Explored RegionsCode1
Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* NeighborhoodCode1
Latent World Models For Intrinsically Motivated ExplorationCode1
Novelty Search in Representational Space for Sample Efficient ExplorationCode1
Occupancy Anticipation for Efficient Exploration and NavigationCode1
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the LoopCode1
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement LearningCode1
See, Hear, Explore: Curiosity via Audio-Visual AssociationCode1
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven ExplorationCode1
Shared Experience Actor-Critic for Multi-Agent Reinforcement LearningCode1
Scaling MAP-Elites to Deep NeuroevolutionCode1
Optimistic Exploration even with a Pessimistic InitialisationCode1
Meta Reinforcement Learning with Autonomous Inference of Subtask DependenciesCode1
Self-Supervised Exploration via DisagreementCode1
Learning Exploration Policies for NavigationCode1
Model-Based Active ExplorationCode1
Automatic chemical design using a data-driven continuous representation of moleculesCode1
MOORL: A Framework for Integrating Offline-Online Reinforcement Learning—0
Go-Browse: Training Web Agents with Structured Exploration—0
DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience—0
WoMAP: World Models For Embodied Open-Vocabulary Object Localization—0
HelixDesign-Binder: A Scalable Production-Grade Platform for Binder Design Built on HelixFold3—0
DISCOVER: Automated Curricula for Sparse-Reward Reinforcement LearningCode0
STAR-R1: Spacial TrAnsformation Reasoning by Reinforcing Multimodal LLMsCode0
Comparative Analysis of Black-Box Optimization Methods for Weather Intervention Design—0
IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-TuningCode0
Language Agents Mirror Human Causal Reasoning Biases. How Can We Help Them Think Like Scientists?—0
Distilling Realizable Students from Unrealizable Teachers—0
Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning—0
Interpretable SHAP-bounded Bayesian Optimization for Underwater Acoustic Metamaterial Coating Design—0
An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm—0
Aerial Active STAR-RIS-assisted Satellite-Terrestrial Covert Communications—0
Lumos: Efficient Performance Modeling and Estimation for Large-scale LLM Training—0
Memetic Search for Green Vehicle Routing Problem with Private Capacitated Refueling Stations—0
From Automation to Autonomy in Smart Manufacturing: A Bayesian Optimization Framework for Modeling Multi-Objective Experimentation and Sequential Decision Making—0
Entropy-guided sequence weighting for efficient exploration in RL-based LLM fine-tuning—0
Maya: Optimizing Deep Learning Training Workloads using Emulated Virtual Accelerators—0
FALCONEye: Finding Answers and Localizing Content in ONE-hour-long videos with multi-modal LLMs—0
KEA: Keeping Exploration Alive by Proactively Coordinating Exploration Strategies—0
CAE: Repurposing the Critic as an Explorer in Deep Reinforcement Learning—0
Disentangling Uncertainties by Learning Compressed Data RepresentationCode0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
HyperArm Bandit Optimization: A Novel approach to Hyperparameter Optimization and an Analysis of Bandit Algorithms in Stochastic and Adversarial Settings—0
Is a Good Foundation Necessary for Efficient Reinforcement Learning? The Computational Role of the Base Model in Exploration—0
Reward-Centered ReST-MCTS: A Robust Decision-Making Framework for Robotic Manipulation in High Uncertainty EnvironmentsCode0
Probabilistic Insights for Efficient Exploration Strategies in Reinforcement Learning—0
A Transformer Model for Predicting Chemical Reaction Products from Generic Templates—0
On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach—0
Synergistic Fusion of Multi-Source Knowledge via Evidence Theory for High-Entropy Alloy Discovery—0
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