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 1121–1130 of 15113 papers

TitleStatusHype
ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement LearningCode1
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint GeneratorsCode1
A Deep Reinforcement Learning Approach to First-Order Logic Theorem ProvingCode1
Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid DispatchingCode1
Computational Performance of Deep Reinforcement Learning to find Nash EquilibriaCode1
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
Concise Reasoning via Reinforcement LearningCode1
Compositional Reinforcement Learning from Logical SpecificationsCode1
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor RepresentationCode1
CompoSuite: A Compositional Reinforcement Learning BenchmarkCode1
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Benchmark Results

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