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 961–970 of 15113 papers

TitleStatusHype
Transforming Multimodal Models into Action Models for Radiotherapy—0
Training Language Models to Reason EfficientlyCode2
Autotelic Reinforcement Learning: Exploring Intrinsic Motivations for Skill Acquisition in Open-Ended Environments—0
OmniRL: In-Context Reinforcement Learning by Large-Scale Meta-Training in Randomized Worlds—0
CTR-Driven Advertising Image Generation with Multimodal Large Language ModelsCode2
Demystifying Long Chain-of-Thought Reasoning in LLMsCode3
Optimizing Electric Vehicles Charging using Large Language Models and Graph Neural Networks—0
AI-driven materials design: a mini-review—0
Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning—0
Underwater Soft Fin Flapping Motion with Deep Neural Network Based Surrogate ModelCode0
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Benchmark Results

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