SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 351–375 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
Show:102550
← PrevPage 15 of 21Next →

No leaderboard results yet.