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 501–525 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Turbulence Modeling in Large Eddy SimulationsCode1
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly DataCode1
DataLight: Offline Data-Driven Traffic Signal ControlCode1
Barrier Certified Safety Learning Control: When Sum-of-Square Programming Meets Reinforcement LearningCode1
A Composable Specification Language for Reinforcement Learning TasksCode1
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler MitigationCode1
A Comprehensive Survey of Data Augmentation in Visual Reinforcement LearningCode1
Adaptive Transformers in RLCode1
Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loopCode1
Batch Exploration with Examples for Scalable Robotic Reinforcement LearningCode1
A SWAT-based Reinforcement Learning Framework for Crop ManagementCode1
Simplified Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
Asynchronous Methods for Deep Reinforcement LearningCode1
DARTS: Differentiable Architecture SearchCode1
Dataset Reset Policy Optimization for RLHFCode1
Curriculum-based Reinforcement Learning for Distribution System Critical Load RestorationCode1
Curriculum Offline Imitation LearningCode1
CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language ModelsCode1
Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic ApproachCode1
Curriculum-based Asymmetric Multi-task Reinforcement LearningCode1
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain AdaptationCode1
Curious Hierarchical Actor-Critic Reinforcement LearningCode1
CURL: Contrastive Unsupervised Representation Learning for Reinforcement LearningCode1
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement LearningCode1
Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning ApproachCode1
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Benchmark Results

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