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 1101–1125 of 15113 papers

TitleStatusHype
DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learningCode1
Multimodal Knowledge Alignment with Reinforcement LearningCode1
Scalable Multi-Agent Model-Based Reinforcement LearningCode1
History Compression via Language Models in Reinforcement LearningCode1
Reward Uncertainty for Exploration in Preference-based Reinforcement LearningCode1
When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement LearningCode1
Learning to branch with Tree MDPsCode1
Memory-efficient Reinforcement Learning with Value-based Knowledge ConsolidationCode1
ARLO: A Framework for Automated Reinforcement LearningCode1
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam SearchCode1
Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-CommunicationCode1
Time Series Anomaly Detection via Reinforcement Learning-Based Model SelectionCode1
A2C is a special case of PPOCode1
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement LearningCode1
Reachability Constrained Reinforcement LearningCode1
The Primacy Bias in Deep Reinforcement LearningCode1
Deep Reinforcement Learning for Computational Fluid Dynamics on HPC SystemsCode1
Intelligent Reflecting Surface Configurations for Smart Radio Using Deep Reinforcement LearningCode1
VesNet-RL: Simulation-based Reinforcement Learning for Real-World US Probe NavigationCode1
State Encoders in Reinforcement Learning for Recommendation: A Reproducibility StudyCode1
Efficient Risk-Averse Reinforcement LearningCode1
Gamma and Vega Hedging Using Deep Distributional Reinforcement LearningCode1
Learning to Brachiate via Simplified Model ImitationCode1
DxFormer: A Decoupled Automatic Diagnostic System Based on Decoder-Encoder Transformer with Dense Symptom RepresentationsCode1
Multivariate Prediction Intervals for Random ForestsCode1
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Benchmark Results

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