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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2201–2225 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
Generalized Population-Based Training for Hyperparameter Optimization in Reinforcement LearningCode0
Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation—0
Dataset Reset Policy Optimization for RLHFCode1
FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning—0
Efficient Duple Perturbation Robustness in Low-rank MDPs—0
Enhancing Policy Gradient with the Polyak Step-Size Adaption—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
How Consistent are Clinicians? Evaluating the Predictability of Sepsis Disease Progression with Dynamics ModelsCode1
Rethinking Out-of-Distribution Detection for Reinforcement Learning: Advancing Methods for Evaluation and DetectionCode0
Dual Ensemble Kalman Filter for Stochastic Optimal Control—0
Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery—0
UAV-Assisted Enhanced Coverage and Capacity in Dynamic MU-mMIMO IoT Systems: A Deep Reinforcement Learning Approach—0
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis—0
Diverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning—0
Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators(BTMG) Approach for Failure Management—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
Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning—0
Compositional Conservatism: A Transductive Approach in Offline Reinforcement LearningCode0
Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology—0
Continual Policy Distillation of Reinforcement Learning-based Controllers for Soft Robotic In-Hand ManipulationCode0
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Benchmark Results

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