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 11111120 of 15113 papers

TitleStatusHype
ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement LearningCode1
Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid DispatchingCode1
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
Consistency Models as a Rich and Efficient Policy Class for Reinforcement LearningCode1
Continual Learning with Gated Incremental Memories for sequential data processingCode1
CoRL: Environment Creation and Management Focused on System IntegrationCode1
Decoupling Strategy and Generation in Negotiation DialoguesCode1
Compile Scene Graphs with Reinforcement LearningCode1
Compiler Optimization for Quantum Computing Using Reinforcement LearningCode1
Compositional Reinforcement Learning from Logical SpecificationsCode1
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Benchmark Results

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