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 2871–2880 of 15113 papers

TitleStatusHype
Augmented Lagrangian-Based Safe Reinforcement Learning Approach for Distribution System Volt/VAR Control—0
AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference—0
ACERAC: Efficient reinforcement learning in fine time discretization—0
Augmented Intelligence in Smart Intersections: Local Digital Twins-Assisted Hybrid Autonomous Driving—0
A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning—0
AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning—0
A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems—0
AirCapRL: Autonomous Aerial Human Motion Capture using Deep Reinforcement Learning—0
Adaptive control of a mechatronic system using constrained residual reinforcement learning—0
A Tutorial Introduction to Reinforcement Learning—0
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Benchmark Results

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