SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 3801–3850 of 15113 papers

TitleStatusHype
Optimizing Wireless Discontinuous Reception via MAC Signaling Learning—0
Oralytics Reinforcement Learning AlgorithmCode0
Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols—0
Order-Optimal Instance-Dependent Bounds for Offline Reinforcement Learning with Preference Feedback—0
Autonomous navigation of catheters and guidewires in mechanical thrombectomy using inverse reinforcement learning—0
More Efficient Randomized Exploration for Reinforcement Learning via Approximate SamplingCode0
Quantum Compiling with Reinforcement Learning on a Superconducting Processor—0
A Systematization of the Wagner Framework: Graph Theory Conjectures and Reinforcement LearningCode0
Physics-informed Imitative Reinforcement Learning for Real-world Driving—0
Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions—0
Constructing Ancestral Recombination Graphs through Reinforcement Learning—0
Constrained Reinforcement Learning with Average Reward Objective: Model-Based and Model-Free Algorithms—0
Run Time Assured Reinforcement Learning for Six Degree-of-Freedom Spacecraft Inspection—0
Adding Conditional Control to Diffusion Models with Reinforcement Learning—0
Design of Interacting Particle Systems for Fast Linear Quadratic RL—0
Generating and Evolving Reward Functions for Highway Driving with Large Language Models—0
UniZero: Generalized and Efficient Planning with Scalable Latent World Models—0
Finite-Time Analysis of Simultaneous Double Q-learning—0
ROAR: Reinforcing Original to Augmented Data Ratio Dynamics for Wav2Vec2.0 Based ASR—0
Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models—0
Adaptive Actor-Critic Based Optimal Regulation for Drift-Free Uncertain Nonlinear Systems—0
Data-driven modeling and supervisory control system optimization for plug-in hybrid electric vehicles—0
SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets—0
DiffPoGAN: Diffusion Policies with Generative Adversarial Networks for Offline Reinforcement Learning—0
e-COP : Episodic Constrained Optimization of Policies—0
CIMRL: Combining IMitation and Reinforcement Learning for Safe Autonomous Driving—0
Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement LearningCode0
Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning—0
When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal AbstractionsCode0
Reinforcement Learning to Disentangle Multiqubit Quantum States from Partial ObservationsCode0
RILe: Reinforced Imitation Learning—0
Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challenges—0
Hybrid Reinforcement Learning from Offline Observation Alone—0
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning—0
Unifying Interpretability and Explainability for Alzheimer's Disease Progression PredictionCode0
Enhanced Gene Selection in Single-Cell Genomics: Pre-Filtering Synergy and Reinforced Optimization—0
Learning Reward and Policy Jointly from Demonstration and Preference Improves Alignment—0
Integrating Domain Knowledge for handling Limited Data in Offline RL—0
CHARME: A chain-based reinforcement learning approach for the minor embedding problem—0
EXPIL: Explanatory Predicate Invention for Learning in GamesCode0
Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning—0
Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance OptimizationCode0
Decoupling regularization from the action spaceCode0
Is Value Functions Estimation with Classification Plug-and-play for Offline Reinforcement Learning?Code0
STARLING: Self-supervised Training of Text-based Reinforcement Learning Agent with Large Language ModelsCode0
Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RLCode0
Enhanced Flight Envelope Protection: A Novel Reinforcement Learning Approach—0
Diffusion-based Reinforcement Learning for Dynamic UAV-assisted Vehicle Twins Migration in Vehicular Metaverses—0
Optimizing Automatic Differentiation with Deep Reinforcement Learning—0
Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified