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 3076–3100 of 15113 papers

TitleStatusHype
A Survey on Causal Reinforcement Learning—0
A Survey of Text Games for Reinforcement Learning informed by Natural Language—0
A Geometric Perspective on Self-Supervised Policy Adaptation—0
Adaptable image quality assessment using meta-reinforcement learning of task amenability—0
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning—0
A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models—0
A Geometric Perspective on Optimal Representations for Reinforcement Learning—0
A Survey of Reinforcement Learning Techniques: Strategies, Recent Development, and Future Directions—0
A Survey of Reinforcement Learning Informed by Natural Language—0
Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning—0
Query Rewriting for Effective Misinformation Discovery—0
Deep Reinforcement Fuzzing—0
Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information—0
A Survey of Reinforcement Learning from Human Feedback—0
A Survey of Reinforcement Learning for Optimization in Automation—0
Age of Information Aware VNF Scheduling in Industrial IoT Using Deep Reinforcement Learning—0
A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective—0
A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments—0
Adaptable Automation with Modular Deep Reinforcement Learning and Policy Transfer—0
A Survey of Multi-Agent Deep Reinforcement Learning with Communication—0
A review of motion planning algorithms for intelligent robotics—0
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC—0
A Survey of Meta-Reinforcement Learning—0
Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound—0
AdapShare: An RL-Based Dynamic Spectrum Sharing Solution for O-RAN—0
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Benchmark Results

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