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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 351–400 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
Model-free optimal controller for discrete-time Markovian jump linear systems: A Q-learning approach—0
QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction—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 with Q-learning: the impact of information perception—0
Evolution of cooperation in the public goods game with Q-learning—0
Multi-Agent Deep Reinforcement Learning for Energy Efficient Multi-Hop STAR-RIS-Assisted Transmissions—0
QT-TDM: Planning With Transformer Dynamics Model and Autoregressive Q-Learning—0
Long-term Fairness in Ride-Hailing Platform—0
Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts—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
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RLCode0
Coverage-aware and Reinforcement Learning Using Multi-agent Approach for HD Map QoS in a Realistic Environment—0
Reinforcement Learning: Tutorial and Survey—0
An Agile Adaptation Method for Multi-mode Vehicle Communication Networks—0
Deep Reinforcement Learning for Multi-Objective Optimization: Enhancing Wind Turbine Energy Generation while Mitigating Noise Emissions—0
Optimistic Q-learning for average reward and episodic reinforcement learning—0
Solving the Model Unavailable MARE using Q-Learning Algorithm—0
Misspecified Q-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error—0
Cooperative Reward Shaping for Multi-Agent Pathfinding—0
Exploration in Knowledge Transfer Utilizing Reinforcement Learning—0
PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization—0
PID Accelerated Temporal Difference Algorithms—0
Periodic agent-state based Q-learning for POMDPs—0
A Multi-Step Minimax Q-learning Algorithm for Two-Player Zero-Sum Markov GamesCode0
Unified continuous-time q-learning for mean-field game and mean-field control problems—0
Robust Q-Learning for finite ambiguity setsCode0
Artificial Intelligence and Algorithmic Price Collusion in Two-sided Markets—0
Continuous-time q-Learning for Jump-Diffusion Models under Tsallis Entropy—0
Configuring Transmission Thresholds in IIoT Alarm Scenarios for Energy-Efficient Event Reporting—0
Two-Step Q-Learning—0
A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimization—0
Model-based Offline Reinforcement Learning with Lower Expectile Q-Learning—0
Towards Secure and Efficient Data Scheduling for Vehicular Social Networks—0
Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control PriorsCode0
Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks—0
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