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Q-Learning

The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

( Image credit: Playing Atari with Deep Reinforcement Learning )

Papers

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Showing 351–375 of 1918 papers

TitleStatusHype
Dynamic operator management in meta-heuristics using reinforcement learning: an application to permutation flowshop scheduling problems—0
Optimizing TD3 for 7-DOF Robotic Arm Grasping: Overcoming Suboptimality with Exploration-Enhanced Contrastive Learning—0
Can LLM be a Good Path Planner based on Prompt Engineering? Mitigating the Hallucination for Path Planning—0
Deviations from the Nash equilibrium and emergence of tacit collusion in a two-player optimal execution game with reinforcement learning—0
GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits—0
Improved Q-learning based Multi-hop Routing for UAV-Assisted Communication—0
A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts—0
Variance-Reduced Cascade Q-learning: Algorithms and Sample Complexity—0
A Geometric Nash Approach in Tuning the Learning Rate in Q-Learning Algorithm—0
Crowd Intelligence for Early Misinformation Prediction on Social MediaCode0
QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction—0
Model-free optimal controller for discrete-time Markovian jump linear systems: A Q-learning approach—0
Whittle's index-based age-of-information minimization in multi-energy harvesting source networks—0
Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response—0
Multi-Objective Deep Reinforcement Learning for Optimisation in Autonomous Systems—0
Multi-agent Assessment with QoS Enhancement for HD Map Updates in a Vehicular Network—0
Evolution of cooperation in the public goods game with Q-learning—0
Evolution of cooperation with Q-learning: the impact of information perception—0
QT-TDM: Planning With Transformer Dynamics Model and Autoregressive Q-Learning—0
Multi-Agent Deep Reinforcement Learning for Energy Efficient Multi-Hop STAR-RIS-Assisted Transmissions—0
Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts—0
Long-term Fairness in Ride-Hailing Platform—0
In Search for Architectures and Loss Functions in Multi-Objective Reinforcement Learning—0
MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search—0
Evaluation of Reinforcement Learning for Autonomous Penetration Testing using A3C, Q-learning and DQN—0
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