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 2751–2760 of 15113 papers

TitleStatusHype
Dexterous Manipulation through Imitation Learning: A Survey—0
Learning Dual-Arm Coordination for Grasping Large Flat Objects—0
Improving Mixed-Criticality Scheduling with Reinforcement Learning—0
Offline and Distributional Reinforcement Learning for Wireless Communications—0
Integrating Human Knowledge Through Action Masking in Reinforcement Learning for Operations Research—0
Reasoning Under 1 Billion: Memory-Augmented Reinforcement Learning for Large Language ModelsCode0
Adapting World Models with Latent-State Dynamics Residuals—0
Inference-Time Scaling for Generalist Reward Modeling—0
MAD: A Magnitude And Direction Policy Parametrization for Stability Constrained Reinforcement LearningCode0
Reinforcement Learning for Solving the Pricing Problem in Column Generation: Applications to Vehicle Routing—0
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Benchmark Results

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