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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 476–500 of 1918 papers

TitleStatusHype
Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation—0
Applying Reinforcement Learning to Option Pricing and Hedging—0
Optimal Control of District Cooling Energy Plant with Reinforcement Learning and MPC—0
PGDQN: Preference-Guided Deep Q-NetworkCode1
A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback—0
Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation—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
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement LearningCode1
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
Double Deep Q-Learning-based Path Selection and Service Placement for Latency-Sensitive Beyond 5G Applications—0
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions—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
Harnessing Deep Q-Learning for Enhanced Statistical Arbitrage in High-Frequency Trading: A Comprehensive Exploration—0
Dynamic control of self-assembly of quasicrystalline structures through reinforcement learningCode0
Reasoning with Latent Diffusion in Offline Reinforcement LearningCode1
A Q-learning Approach for Adherence-Aware Recommendations—0
Career Path Recommendations for Long-term Income Maximization: A Reinforcement Learning Approach—0
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