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 4801–4825 of 15113 papers

TitleStatusHype
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
Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing—0
Reinforcement Learning for Adaptive Mesh Refinement—0
Reinforcement Learning for Adaptive Video Compressive Sensing—0
Reinforcement Learning for a Discrete-Time Linear-Quadratic Control Problem with an Application—0
Reinforcement learning for Admission Control in 5G Wireless Networks—0
Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding—0
Reinforcement Learning for Agile Active Target Sensing with a UAV—0
Reinforcement Learning for Aligning Large Language Models Agents with Interactive Environments: Quantifying and Mitigating Prompt Overfitting—0
Reinforcement Learning for AMR Charging Decisions: The Impact of Reward and Action Space Design—0
Reinforcement learning for anisotropic p-adaptation and error estimation in high-order solvers—0
Reinforcement Learning for Assignment problem—0
Reinforcement Learning for Assignment Problem with Time Constraints—0
Reinforcement Learning for Autonomous Defence in Software-Defined Networking—0
Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships—0
Reinforcement learning for bandwidth estimation and congestion control in real-time communications—0
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Benchmark Results

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