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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 601–625 of 1918 papers

TitleStatusHype
Applying Reinforcement Learning to Option Pricing and Hedging—0
Optimal Control of District Cooling Energy Plant with Reinforcement Learning and MPC—0
Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation—0
A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback—0
Using Reinforcement Learning to Optimize Responses in Care Processes: A Case Study on Aggression Incidents—0
Pre-training with Synthetic Data Helps Offline Reinforcement LearningCode0
Reinforcement learning adaptive fuzzy controller for lighting systems: application to aircraft cabin—0
Multi-Bellman operator for convergence of Q-learning with linear function approximation—0
Decoding trust: A reinforcement learning perspective—0
Adapting Double Q-Learning for Continuous Reinforcement Learning—0
UAV Swarm Deployment and Trajectory for 3D Area Coverage via Reinforcement Learning—0
Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions—0
Differentiable Quantum Architecture Search for Quantum Reinforcement Learning—0
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions—0
Double Deep Q-Learning-based Path Selection and Service Placement for Latency-Sensitive Beyond 5G Applications—0
Self-Sustaining Multiple Access with Continual Deep Reinforcement Learning for Dynamic Metaverse Applications—0
Data-Driven H-infinity Control with a Real-Time and Efficient Reinforcement Learning Algorithm: An Application to Autonomous Mobility-on-Demand Systems—0
Dynamic control of self-assembly of quasicrystalline structures through reinforcement learningCode0
Harnessing Deep Q-Learning for Enhanced Statistical Arbitrage in High-Frequency Trading: A Comprehensive Exploration—0
A Q-learning Approach for Adherence-Aware Recommendations—0
Career Path Recommendations for Long-term Income Maximization: A Reinforcement Learning Approach—0
Convex Q Learning in a Stochastic Environment: Extended Version—0
Physics-Based Trajectory Design for Cellular-Connected UAV in Rainy Environments Based on Deep Reinforcement Learning—0
Multi Agent DeepRL based Joint Power and Subchannel Allocation in IAB networks—0
Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic ArmCode0
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