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 3201–3210 of 15113 papers

TitleStatusHype
Machine-learning based noise characterization and correction on neutral atoms NISQ devices—0
Learning to Modulate pre-trained Models in RLCode1
InterCode: Standardizing and Benchmarking Interactive Coding with Execution FeedbackCode2
Augmenting Control over Exploration Space in Molecular Dynamics Simulators to Streamline De Novo Analysis through Generative Control Policies—0
Decentralized Multi-Robot Formation Control Using Reinforcement Learning—0
Supervised Pretraining Can Learn In-Context Reinforcement Learning—0
Estimating player completion rate in mobile puzzle games using reinforcement learning—0
Multivariate Time Series Early Classification Across Channel and Time DimensionsCode0
ChiPFormer: Transferable Chip Placement via Offline Decision Transformer—0
A Framework for dynamically meeting performance objectives on a service mesh—0
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Benchmark Results

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