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 11811190 of 15113 papers

TitleStatusHype
Energy Pricing in P2P Energy Systems Using Reinforcement LearningCode1
Evaluating Soccer Player: from Live Camera to Deep Reinforcement LearningCode1
Autonomous Reinforcement Learning: Formalism and BenchmarkingCode1
BIMRL: Brain Inspired Meta Reinforcement LearningCode1
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple ConstraintsCode1
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement LearningCode1
Efficient Risk-Averse Reinforcement LearningCode1
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement LearningCode1
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse ShapesCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
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Benchmark Results

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