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 811–820 of 15113 papers

TitleStatusHype
A Text-based Deep Reinforcement Learning Framework for Interactive RecommendationCode1
Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous DrivingCode1
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement LearningCode1
CFR-RL: Traffic Engineering with Reinforcement Learning in SDNCode1
Challenges for Reinforcement Learning in Quantum Circuit DesignCode1
Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning SystemsCode1
Active Inference for Stochastic ControlCode1
Character Controllers Using Motion VAEsCode1
De novo PROTAC design using graph-based deep generative modelsCode1
Design and implementation of an environment for Learning to Run a Power Network (L2RPN)Code1
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Benchmark Results

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