Papers Efficient Exploration
“Efficient Exploration” 태그가 달린 논문 514편 · 필터 해제
MOORL: A Framework for Integrating Offline-Online Reinforcement Learning
Sample efficiency and exploration remain critical challenges in Deep Reinforcement Learning (DRL), particularly in complex domains. Offline RL, which enables agents to learn optimal policies from static, pre-collected da…
D4RLDeep Reinforcement LearningEfficient ExplorationOffline RL+2DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience
Symbolic regression is a fundamental tool for discovering interpretable mathematical expressions from data, with broad applications across scientific and engineering domains. Recently, large language models (LLMs) have d…
Efficient ExplorationEquation DiscoveryregressionSymbolic Regression+1Go-Browse: Training Web Agents with Structured Exploration
One of the fundamental problems in digital agents is their lack of understanding of their environment. For instance, a web browsing agent may get lost in unfamiliar websites, uncertain what pages must be visited to achie…
Efficient ExplorationLanguage ModelingLanguage ModellingWoMAP: World Models For Embodied Open-Vocabulary Object Localization
Language-instructed active object localization is a critical challenge for robots, requiring efficient exploration of partially observable environments. However, state-of-the-art approaches either struggle to generalize …
Active Object LocalizationEfficient ExplorationImitation LearningObject+1MermaidFlow: Redefining Agentic Workflow Generation via Safety-Constrained Evolutionary Programming
Despite the promise of autonomous agentic reasoning, existing workflow generation methods frequently produce fragile, unexecutable plans due to unconstrained LLM-driven construction. We introduce MermaidFlow, a framework…
DiversityEfficient ExplorationHelixDesign-Binder: A Scalable Production-Grade Platform for Binder Design Built on HelixFold3
Protein binder design is central to therapeutics, diagnostics, and synthetic biology, yet practical deployment remains challenging due to fragmented workflows, high computational costs, and complex tool integration. We p…
BenchmarkingEfficient ExplorationDISCOVER: Automated Curricula for Sparse-Reward Reinforcement Learning
Sparse-reward reinforcement learning (RL) can model a wide range of highly complex tasks. Solving sparse-reward tasks is RL's core premise - requiring efficient exploration coupled with long-horizon credit assignment - a…
Efficient Explorationreinforcement-learningReinforcement LearningReinforcement Learning (RL)STAR-R1: Spacial TrAnsformation Reasoning by Reinforcing Multimodal LLMs
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across diverse tasks, yet they lag significantly behind humans in spatial reasoning. We investigate this gap through Transformation-Drive…
Efficient ExplorationReinforcement Learning (RL)Spatial ReasoningVisual ReasoningComparative Analysis of Black-Box Optimization Methods for Weather Intervention Design
As climate change increases the threat of weather-related disasters, research on weather control is gaining importance. The objective of weather control is to mitigate disaster risks by administering interventions with o…
Bayesian OptimizationEfficient ExplorationModel Predictive ControlIN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-Tuning
Imitation learning (IL) and reinforcement learning (RL) each offer distinct advantages for robotics policy learning: IL provides stable learning from demonstrations, and RL promotes generalization through exploration. Wh…
Efficient ExplorationImitation LearningOpenAI GymReinforcement Learning (RL)+1Language Agents Mirror Human Causal Reasoning Biases. How Can We Help Them Think Like Scientists?
Language model (LM) agents are increasingly used as autonomous decision-makers who need to actively gather information to guide their decisions. A crucial cognitive skill for such agents is the efficient exploration and …
Efficient ExplorationDistilling Realizable Students from Unrealizable Teachers
We study policy distillation under privileged information, where a student policy with only partial observations must learn from a teacher with full-state access. A key challenge is information asymmetry: the student can…
Efficient ExplorationImitation LearningCredit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning
Training cooperative agents in sparse-reward scenarios poses significant challenges for multi-agent reinforcement learning (MARL). Without clear feedback on actions at each step in sparse-reward setting, previous methods…
Efficient ExplorationMulti-agent Reinforcement LearningInterpretable SHAP-bounded Bayesian Optimization for Underwater Acoustic Metamaterial Coating Design
We developed an interpretability informed Bayesian optimization framework to optimize underwater acoustic coatings based on polyurethane elastomers with embedded metamaterial features. A data driven model was employed to…
Bayesian OptimizationEfficient ExplorationAn Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm
The recent global spread of monkeypox, particularly in regions where it has not historically been prevalent, has raised significant public health concerns. Early and accurate diagnosis is critical for effective disease m…
DiagnosticDimensionality ReductionEfficient ExplorationMonkeyPox Diagnosis+1ForesightNav: Learning Scene Imagination for Efficient Exploration
Understanding how humans leverage prior knowledge to navigate unseen environments while making exploratory decisions is essential for developing autonomous robots with similar abilities. In this work, we propose Foresigh…
Efficient ExplorationNavigateAerial Active STAR-RIS-assisted Satellite-Terrestrial Covert Communications
An integration of satellites and terrestrial networks is crucial for enhancing performance of next generation communication systems. However, the networks are hindered by the long-distance path loss and security risks in…
Deep Reinforcement LearningDenoisingEfficient ExplorationLumos: Efficient Performance Modeling and Estimation for Large-scale LLM Training
Training LLMs in distributed environments presents significant challenges due to the complexity of model execution, deployment systems, and the vast space of configurable strategies. Although various optimization techniq…
Efficient ExplorationMemetic Search for Green Vehicle Routing Problem with Private Capacitated Refueling Stations
The green vehicle routing problem with private capacitated alternative fuel stations (GVRP-PCAFS) extends the traditional green vehicle routing problem by considering refueling stations limited capacity, where a limited …
Efficient ExplorationFrom Automation to Autonomy in Smart Manufacturing: A Bayesian Optimization Framework for Modeling Multi-Objective Experimentation and Sequential Decision Making
Discovering novel materials with desired properties is essential for driving innovation. Industry 4.0 and smart manufacturing have promised transformative advances in this area through real-time data integration and auto…
Bayesian OptimizationData IntegrationDecision MakingEfficient Exploration+1