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

Showing 13761400 of 15113 papers

TitleStatusHype
Reinforcement Learning based Condition-oriented Maintenance Scheduling for Flow Line SystemsCode1
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language ModelsCode1
Responsive Regulation of Dynamic UAV Communication Networks Based on Deep Reinforcement LearningCode1
Robust Risk-Aware Reinforcement LearningCode1
Settling the Variance of Multi-Agent Policy GradientsCode1
End-to-End Urban Driving by Imitating a Reinforcement Learning CoachCode1
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement LearningCode1
Aspect Sentiment Triplet Extraction Using Reinforcement LearningCode1
Continual Backprop: Stochastic Gradient Descent with Persistent RandomnessCode1
Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action SpacesCode1
VeRLPy: Python Library for Verification of Digital Designs with Reinforcement LearningCode1
Paint Transformer: Feed Forward Neural Painting with Stroke PredictionCode1
Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI EconomistCode1
The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement LearningCode1
An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement LearningCode1
Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time ViolationsCode1
Scalable Multi-agent Reinforcement Learning Algorithm for Wireless NetworksCode1
Strategically Efficient Exploration in Competitive Multi-agent Reinforcement LearningCode1
Finding Failures in High-Fidelity Simulation using Adaptive Stress Testing and the Backward AlgorithmCode1
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare SettingsCode1
Accelerating Quadratic Optimization with Reinforcement LearningCode1
MarsExplorer: Exploration of Unknown Terrains via Deep Reinforcement Learning and Procedurally Generated EnvironmentsCode1
Demonstration-Guided Reinforcement Learning with Learned SkillsCode1
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement LearningCode1
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement LearningCode1
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Benchmark Results

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