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 671–680 of 15113 papers

TitleStatusHype
Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven OptimizationCode1
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive AgentsCode1
Reduced Policy Optimization for Continuous Control with Hard ConstraintsCode1
METRA: Scalable Unsupervised RL with Metric-Aware AbstractionCode1
Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration BiasCode1
Aligning Language Models with Human Preferences via a Bayesian ApproachCode1
GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning ModelsCode1
Safe Deep Policy AdaptationCode1
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement LearningCode1
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced DatasetsCode1
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Benchmark Results

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