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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9051–9075 of 15113 papers

TitleStatusHype
Reinforcement Learning-based Wavefront Sensorless Adaptive Optics Approaches for Satellite-to-Ground Laser Communication—0
Reinforcement Learning Beyond Expectation—0
Reinforcement Learning, Bit by Bit—0
Reinforcement Learning by Guided Safe Exploration—0
Reinforcement Learning Constrained Beam Search for Parameter Optimization of Paper Drying Under Flexible Constraints—0
Reinforcement Learning Control of a Biomechanical Model of the Upper Extremity—0
Reinforcement Learning Control of Constrained Dynamic Systems with Uniformly Ultimate Boundedness Stability Guarantee—0
Reinforcement Learning Control of Robotic Knee with Human in the Loop by Flexible Policy Iteration—0
Reinforcement Learning-driven Data-intensive Workflow Scheduling for Volunteer Edge-Cloud—0
PaccMann^RL: Designing anticancer drugs from transcriptomic data via reinforcement learning—0
Reinforcement Learning Driven Heuristic Optimization—0
Reinforcement Learning-driven Information Seeking: A Quantum Probabilistic Approach—0
Reinforcement Learning-Driven Test Generation for Android GUI Applications using Formal Specifications—0
Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence—0
Reinforcement learning enabled cooperative spectrum sensing in cognitive radio networks—0
Reinforcement Learning-Enabled Decision-Making Strategies for a Vehicle-Cyber-Physical-System in Connected Environment—0
Reinforcement Learning-Enabled Reliable Wireless Sensor Networks in Dynamic Underground Environments—0
Reinforcement Learning-enabled Satellite Constellation Reconfiguration and Retasking for Mission-Critical Applications—0
Reinforcement Learning Enhanced Explainer for Graph Neural Networks—0
Reinforcement learning-enhanced genetic algorithm for wind farm layout optimization—0
Reinforcement Learning-Enhanced Procedural Generation for Dynamic Narrative-Driven AR Experiences—0
Reinforcement Learning Environment with LLM-Controlled Adversary in D&D 5th Edition Combat—0
Reinforcement Learning Experience Reuse with Policy Residual Representation—0
Reinforcement Learning Finetunes Small Subnetworks in Large Language Models—0
Reinforcement Learning for Active Matter—0
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Benchmark Results

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