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 51–60 of 15113 papers

TitleStatusHype
IRanker: Towards Ranking Foundation ModelCode1
Complex Model Transformations by Reinforcement Learning with Uncertain Human GuidanceCode0
Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative ControlCode0
OctoThinker: Mid-training Incentivizes Reinforcement Learning ScalingCode2
DiffuCoder: Understanding and Improving Masked Diffusion Models for Code GenerationCode4
Asymmetric REINFORCE for off-Policy Reinforcement Learning: Balancing positive and negative rewards—0
Partially Observable Residual Reinforcement Learning for PV-Inverter-Based Voltage Control in Distribution GridsCode0
KnowRL: Exploring Knowledgeable Reinforcement Learning for FactualityCode1
A Comparative Analysis of Reinforcement Learning and Conventional Deep Learning Approaches for Bearing Fault Diagnosis—0
Hierarchical Reinforcement Learning and Value Optimization for Challenging Quadruped Locomotion—0
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Benchmark Results

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