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 4051–4075 of 15113 papers

TitleStatusHype
What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement LearningCode0
Offline Trajectory Generalization for Offline Reinforcement Learning—0
Course Recommender Systems Need to Consider the Job MarketCode0
Automated Discovery of Functional Actual Causes in Complex Environments—0
Effective Reinforcement Learning Based on Structural Information Principles—0
Autonomous Path Planning for Intercostal Robotic Ultrasound Imaging Using Reinforcement Learning—0
Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement LearningCode0
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning—0
The Feasibility of Constrained Reinforcement Learning Algorithms: A Tutorial Study—0
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
Generalized Population-Based Training for Hyperparameter Optimization in Reinforcement LearningCode0
Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation—0
Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains—0
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
On the Sample Efficiency of Abstractions and Potential-Based Reward Shaping in Reinforcement Learning—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
Dual Ensemble Kalman Filter for Stochastic Optimal Control—0
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
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Benchmark Results

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