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 9676–9700 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Process Control: A Primer for Beginners—0
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
Deep reinforcement learning for quantum multiparameter estimation—0
Deep reinforcement learning for RAN optimization and control—0
Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments—0
Deep Reinforcement Learning for Resource Management in Network Slicing—0
Deep Reinforcement Learning for RIS-Assisted FD Systems: Single or Distributed RIS?—0
Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates—0
Deep Reinforcement Learning for Robotic Manipulation-The state of the art—0
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment—0
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes—0
Deep Reinforcement Learning for Routing a Heterogeneous Fleet of Vehicles—0
Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers—0
Deep Reinforcement Learning for Scalable Multiagent Spacecraft Inspection—0
Using Deep Reinforcement Learning for mmWave Real-Time Scheduling—0
Deep reinforcement learning for scheduling in large-scale networked control systems—0
Deep Reinforcement Learning for Scheduling in Cellular Networks—0
Deep reinforcement learning for search, recommendation, and online advertising: a survey—0
Deep Reinforcement Learning for Shared Autonomous Vehicles (SAV) Fleet Management—0
Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks—0
Deep Reinforcement Learning for Single-Shot Diagnosis and Adaptation in Damaged Robots—0
Deep Reinforcement Learning for Small Bowel Path Tracking using Different Types of Annotations—0
A Review of Deep Reinforcement Learning for Smart Building Energy Management—0
Deep Reinforcement Learning for Smart Home Energy Management—0
Deep Reinforcement Learning for Sponsored Search Real-time Bidding—0
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Benchmark Results

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