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 681–690 of 15113 papers

TitleStatusHype
Continuous control with deep reinforcement learningCode1
Adversarial Soft Advantage Fitting: Imitation Learning without Policy OptimizationCode1
Continuous MDP Homomorphisms and Homomorphic Policy GradientCode1
Continuous-Time Model-Based Reinforcement LearningCode1
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
AWAC: Accelerating Online Reinforcement Learning with Offline DatasetsCode1
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
Control-Informed Reinforcement Learning for Chemical ProcessesCode1
Constructions in combinatorics via neural networksCode1
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Benchmark Results

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