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 1321–1330 of 15113 papers

TitleStatusHype
Safe Driving via Expert Guided Policy OptimizationCode1
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse ShapesCode1
StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement LearningCode1
Offline Reinforcement Learning with Implicit Q-LearningCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
Safe Reinforcement Learning Using Robust Control Barrier FunctionsCode1
A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise DatasetsCode1
MARVEL: Raster Manga Vectorization via Primitive-wise Deep Reinforcement LearningCode1
Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation TasksCode1
Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing ProblemCode1
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Benchmark Results

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