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 9301–9350 of 15113 papers

TitleStatusHype
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
Reinforcement Learning to Rank with Coarse-grained Labels—0
Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP—0
Reinforcement Learning Tracking Control for Robotic Manipulator With Kernel-Based Dynamic Model—0
Reinforcement Learning: Tutorial and Survey—0
Reinforcement Learning Under Algorithmic Triage—0
Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory—0
Non-Stationary Reinforcement Learning: The Blessing of (More) Optimism—0
Reinforcement Learning under Model Mismatch—0
Reinforcement Learning under Partial Observability Guided by Learned Environment Models—0
Reinforcement Learning Under Probabilistic Spatio-Temporal Constraints with Time Windows—0
Reinforcement Learningx2013Based Transient Response Shaping for Microgrids—0
Reinforcement Learning using Augmented Neural Networks—0
Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets—0
Reinforcement Learning using Guided Observability—0
Reinforcement Learning using Kernel-Based Stochastic Factorization—0
Reinforcement Learning Using Quantum Boltzmann Machines—0
Reinforcement Learning via AIXI Approximation—0
Reinforcement Learning via Gaussian Processes with Neural Network Dual Kernels—0
Reinforcement Learning via Reasoning from Demonstration—0
Reinforcement Learning via Replica Stacking of Quantum Measurements for the Training of Quantum Boltzmann Machines—0
Coarse-to-fine Q-Network with Action Sequence for Data-Efficient Robot Learning—0
Reinforcement Learning with Adaptive Curriculum Dynamics Randomization for Fault-Tolerant Robot Control—0
Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation—0
Reinforcement Learning with Almost Sure Constraints—0
Reinforcement Learning with Analogical Similarity to Guide Schema Induction and Attention—0
Reinforcement Learning with Attention that Works: A Self-Supervised Approach—0
Reinforcement Learning with Automated Auxiliary Loss Search—0
Reinforcement Learning with Bayesian Classifiers: Efficient Skill Learning from Outcome Examples—0
Reinforcement Learning with Budget-Constrained Nonparametric Function Approximation for Opportunistic Spectrum Access—0
Reinforcement Learning with Chromatic Networks for Compact Architecture Search—0
Reinforcement Learning with Chromatic Networks—0
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives—0
Reinforcement Learning with Convolutional Reservoir Computing—0
Reinforcement learning with Demonstrations from Mismatched Task under Sparse Reward—0
Reinforcement Learning with Depreciating Assets—0
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Benchmark Results

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