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 8676–8700 of 15113 papers

TitleStatusHype
Q-Learning for Continuous Actions with Cross-Entropy Guided Policies—0
q-Learning in Continuous Time—0
Q-Learning in Regularized Mean-field Games—0
Q-learning with online random forests—0
q-Munchausen Reinforcement Learning—0
Q-NAV: NAV Setting Method based on Reinforcement Learning in Underwater Wireless Networks—0
Q-Networks for Binary Vector Actions—0
QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning—0
QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning—0
QoS-Aware Scheduling in New Radio Using Deep Reinforcement Learning—0
Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning—0
QR-MIX: Distributional Value Function Factorisation for Cooperative Multi-Agent Reinforcement Learning—0
qRRT: Quality-Biased Incremental RRT for Optimal Motion Planning in Non-Holonomic Systems—0
Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning—0
Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison—0
QT-TDM: Planning With Transformer Dynamics Model and Autoregressive Q-Learning—0
Quadratic Q-network for Learning Continuous Control for Autonomous Vehicles—0
Quadruped Locomotion on Non-Rigid Terrain using Reinforcement Learning—0
QuadWBG: Generalizable Quadrupedal Whole-Body Grasping—0
Qualitative Differences Between Evolutionary Strategies and Reinforcement Learning Methods for Control of Autonomous Agents—0
Qualitative Measurements of Policy Discrepancy for Return-Based Deep Q-Network—0
Quality-Aware Multimodal Saliency Detection via Deep Reinforcement Learning—0
Quality-Driven Curation of Remote Sensing Vision-Language Data via Learned Scoring Models—0
Quality of service based radar resource management using deep reinforcement learning—0
Quality of syntactic implication of RL-based sentence summarization—0
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Benchmark Results

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