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 2201–2250 of 15113 papers

TitleStatusHype
Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts—0
SmartPathfinder: Pushing the Limits of Heuristic Solutions for Vehicle Routing Problem with Drones Using Reinforcement Learning—0
WROOM: An Autonomous Driving Approach for Off-Road NavigationCode1
Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation—0
Dataset Reset Policy Optimization for RLHFCode1
Generalized Population-Based Training for Hyperparameter Optimization in Reinforcement LearningCode0
FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning—0
Enhancing Policy Gradient with the Polyak Step-Size Adaption—0
Efficient Duple Perturbation Robustness in Low-rank MDPs—0
Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains—0
On the Sample Efficiency of Abstractions and Potential-Based Reward Shaping in Reinforcement Learning—0
Dual Ensemble Kalman Filter for Stochastic Optimal Control—0
UAV-Assisted Enhanced Coverage and Capacity in Dynamic MU-mMIMO IoT Systems: A Deep Reinforcement Learning Approach—0
Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery—0
Rethinking Out-of-Distribution Detection for Reinforcement Learning: Advancing Methods for Evaluation and DetectionCode0
How Consistent are Clinicians? Evaluating the Predictability of Sepsis Disease Progression with Dynamics ModelsCode1
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis—0
Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators(BTMG) Approach for Failure Management—0
Diverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning—0
Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real TransferCode5
FGAIF: Aligning Large Vision-Language Models with Fine-grained AI Feedback—0
Compositional Conservatism: A Transductive Approach in Offline Reinforcement LearningCode0
Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning—0
Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology—0
Continual Policy Distillation of Reinforcement Learning-based Controllers for Soft Robotic In-Hand ManipulationCode0
A Reinforcement Learning based Reset Policy for CDCL SAT Solvers—0
Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning—0
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term RetentionCode0
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm—0
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning—0
SliceIt! -- A Dual Simulator Framework for Learning Robot Food SlicingCode0
Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation—0
Electric Vehicle Routing Problem for Emergency Power Supply: Towards Telecom Base Station ReliefCode1
Reinforcement Learning in Categorical Cybernetics—0
Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning—0
Asymptotics of Language Model Alignment—0
Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning—0
EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and BenchmarkingCode2
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
Entity-Centric Reinforcement Learning for Object Manipulation from PixelsCode1
MTLight: Efficient Multi-Task Reinforcement Learning for Traffic Signal Control—0
Utilizing Maximum Mean Discrepancy Barycenter for Propagating the Uncertainty of Value Functions in Reinforcement Learning—0
Learning Off-policy with Model-based Intrinsic Motivation For Active Online Exploration—0
Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods—0
Molecular Generative Adversarial Network with Multi-Property Optimization—0
Learning Visual Quadrupedal Loco-Manipulation from Demonstrations—0
CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning—0
Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering—0
Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignment—0
Reinforcement Learning in Agent-Based Market Simulation: Unveiling Realistic Stylized Facts and Behavior—0
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Benchmark Results

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