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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 76–100 of 514 papers

TitleStatusHype
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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