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 2361–2370 of 15113 papers

TitleStatusHype
Large Language Models are Learnable Planners for Long-Term RecommendationCode1
Curiosity-driven Red-teaming for Large Language ModelsCode2
RL-GPT: Integrating Reinforcement Learning and Code-as-policy—0
ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RLCode3
Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain—0
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation—0
Reinforcement Learning and Graph Neural Networks for Probabilistic Risk Assessment—0
Learning to Program Variational Quantum Circuits with Fast Weights—0
Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning—0
reBandit: Random Effects based Online RL algorithm for Reducing Cannabis UseCode0
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Benchmark Results

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