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 821–830 of 15113 papers

TitleStatusHype
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted PrescriptionCode1
Efficient Active Search for Combinatorial Optimization ProblemsCode1
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior RegularizationCode1
Conservative Q-Learning for Offline Reinforcement LearningCode1
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and PlanningCode1
Conservative Offline Distributional Reinforcement LearningCode1
Efficient Pressure: Improving efficiency for signalized intersectionsCode1
Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement LearningCode1
Zero-Shot Reinforcement Learning from Low Quality DataCode1
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Benchmark Results

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