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 5026–5050 of 15113 papers

TitleStatusHype
Reinforcement Learning of Structured Control for Linear Systems with Unknown State Matrix—0
Reinforcement Learning of Theorem Proving—0
Reinforcement Learning of the Prediction Horizon in Model Predictive Control—0
Reinforcement Learning of Two-Issue Negotiation Dialogue Policies—0
Reinforcement Learning on Computational Resource Allocation of Cloud-based Wireless Networks—0
Reinforcement Learning on Encrypted Data—0
Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learning—0
Reinforcement Learning Policy Recommendation for Interbank Network Stability—0
Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Information—0
Reinforcement Learning Portfolio Manager Framework with Monte Carlo Simulation—0
Reinforcement Learning: Prediction, Control and Value Function Approximation—0
Reinforcement Learning Problem Solving with Large Language Models—0
Reinforcement Learning reveals fundamental limits on the mixing of active particles—0
Reinforcement learning reward function in unmanned aerial vehicle control tasks—0
On-demand Cold Start Frequency Reduction with Off-Policy Reinforcement Learning in Serverless Computing—0
Reinforcement Learning (RL) Meets Urban Climate Modeling: Investigating the Efficacy and Impacts of RL-Based HVAC Control—0
Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage—0
Reinforcement Learning State Estimation for High-Dimensional Nonlinear Systems—0
Reinforcement Learning Teachers of Test Time Scaling—0
Reinforcement learning techniques for Outer Loop Link Adaptation in 4G/5G systems—0
Reinforcement Learning through Active Inference—0
Reinforcement Learning To Adapt Speech Enhancement to Instantaneous Input Signal Quality—0
Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation—0
Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems—0
Reinforcement Learning to Optimize the Logistics Distribution Routes of Unmanned Aerial Vehicle—0
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Benchmark Results

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