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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 351–400 of 514 papers

TitleStatusHype
Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion—0
Guarded Policy Optimization with Imperfect Online Demonstrations—0
Guided Exploration for Efficient Relational Model Learning—0
Hands-Free Segmentation of Medical Volumes via Binary Inputs—0
Hashing over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning—0
HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression—0
HelixDesign-Binder: A Scalable Production-Grade Platform for Binder Design Built on HelixFold3—0
Hierarchical reinforcement learning for efficient exploration and transfer—0
HyperArm Bandit Optimization: A Novel approach to Hyperparameter Optimization and an Analysis of Bandit Algorithms in Stochastic and Adversarial Settings—0
Hyper: Hyperparameter Robust Efficient Exploration in Reinforcement Learning—0
IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions—0
Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks—0
Impact of detecting clinical trial elements in exploration of COVID-19 literature—0
Implicit Generative Modeling for Efficient Exploration—0
Improving a State-of-the-Art Heuristic for the Minimum Latency Problem with Data Mining—0
Incentivizing Exploration with Selective Data Disclosure—0
Inferring Hierarchical Structure in Multi-Room Maze Environments—0
Information Content Exploration—0
Interpretable SHAP-bounded Bayesian Optimization for Underwater Acoustic Metamaterial Coating Design—0
Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search—0
Intrinsically Guided Exploration in Meta Reinforcement Learning—0
Intrinsic Rewards for Exploration without Harm from Observational Noise: A Simulation Study Based on the Free Energy Principle—0
Is a Good Foundation Necessary for Efficient Reinforcement Learning? The Computational Role of the Base Model in Exploration—0
Joint channel estimation and data detection in massive MIMO systems based on diffusion models—0
Joint Falsification and Fidelity Settings Optimization for Validation of Safety-Critical Systems: A Theoretical Analysis—0
JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning—0
KEA: Keeping Exploration Alive by Proactively Coordinating Exploration Strategies—0
K-Means Clustering using Tabu Search with Quantized Means—0
Language Agents Mirror Human Causal Reasoning Biases. How Can We Help Them Think Like Scientists?—0
Large-scale signatures of unconsciousness are consistent with a departure from critical dynamics—0
Latent Action Priors for Locomotion with Deep Reinforcement Learning—0
Learn2Hop: Learned Optimization on Rough Landscapes—0
Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks—0
Learning Causal Overhypotheses through Exploration in Children and Computational Models—0
Learning Efficient and Effective Exploration Policies with Counterfactual Meta Policy—0
Efficient Exploration via First-Person Behavior Cloning Assisted Rapidly-Exploring Random Trees—0
Learning Exploration Policies for Model-Agnostic Meta-Reinforcement Learning—0
Learning Index Selection with Structured Action Spaces—0
Learning Memory-Dependent Continuous Control from Demonstrations—0
Learning Off-policy with Model-based Intrinsic Motivation For Active Online Exploration—0
Few-shot_LLM_Synthetic_Data_with_Distribution_MatchingCode0
Learning to Score Behaviors for Guided Policy OptimizationCode0
Meta-Learning for Stochastic Gradient MCMCCode0
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based GamesCode0
Meta-Learning Integration in Hierarchical Reinforcement Learning for Advanced Task ComplexityCode0
Federated Control with Hierarchical Multi-Agent Deep Reinforcement LearningCode0
Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug DesignCode0
A diversity-enhanced genetic algorithm for efficient exploration of parameter spacesCode0
A Variational Approach to Bayesian Phylogenetic InferenceCode0
Count-Based Exploration with the Successor RepresentationCode0
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