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 10511060 of 15113 papers

TitleStatusHype
Concise Reasoning via Reinforcement LearningCode1
Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid DispatchingCode1
ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement LearningCode1
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
Computational Performance of Deep Reinforcement Learning to find Nash EquilibriaCode1
Compile Scene Graphs with Reinforcement LearningCode1
Compiler Optimization for Quantum Computing Using Reinforcement LearningCode1
Compositional Reinforcement Learning from Logical SpecificationsCode1
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
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Benchmark Results

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