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 4021–4030 of 15113 papers

TitleStatusHype
A Survey on Transformers in Reinforcement Learning—0
Hierarchical Reinforcement Learning for RIS-Assisted Energy-Efficient RAN—0
Mathematical Models and Reinforcement Learning based Evolutionary Algorithm Framework for Satellite Scheduling Problem—0
Markov Chain Concentration with an Application in Reinforcement Learning—0
Provable Reset-free Reinforcement Learning by No-Regret Reduction—0
A Deep Reinforcement Learning-Based Controller for Magnetorheological-Damped Vehicle Suspension—0
Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads—0
Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanism—0
Value Enhancement of Reinforcement Learning via Efficient and Robust Trust Region Optimization—0
Reinforcement Learning-Based Air Traffic Deconfliction—0
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Benchmark Results

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