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 2301–2310 of 15113 papers

TitleStatusHype
Curriculum-Guided Antifragile Reinforcement Learning for Secure UAV Deconfliction under Observation-Space Attacks—0
RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment—0
Complex Model Transformations by Reinforcement Learning with Uncertain Human GuidanceCode0
Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative ControlCode0
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
A Comparative Analysis of Reinforcement Learning and Conventional Deep Learning Approaches for Bearing Fault Diagnosis—0
Causal-Aware Intelligent QoE Optimization for VR Interaction with Adaptive Keyframe Extraction—0
Hierarchical Reinforcement Learning and Value Optimization for Challenging Quadruped Locomotion—0
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model—0
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Benchmark Results

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