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 611–620 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Day-to-day Dynamic Tolling in Tradable Credit Schemes—0
RL-based Control of UAS Subject to Significant Disturbance—0
Perception-R1: Pioneering Perception Policy with Reinforcement LearningCode3
Echo Chamber: RL Post-training Amplifies Behaviors Learned in PretrainingCode1
Kimi-VL Technical ReportCode5
Boosting Universal LLM Reward Design through the Heuristic Reward Observation Space Evolution—0
Genetic Programming with Reinforcement Learning Trained Transformer for Real-World Dynamic Scheduling Problems—0
VLM-R1: A Stable and Generalizable R1-style Large Vision-Language ModelCode9
Fast Adaptation with Behavioral Foundation Models—0
Harnessing Equivariance: Modeling Turbulence with Graph Neural NetworksCode1
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Benchmark Results

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