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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 1526–1550 of 1918 papers

TitleStatusHype
Q-Networks for Binary Vector Actions—0
QoS-Aware Power Minimization of Distributed Many-Core Servers using Transfer Q-Learning—0
Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning—0
Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things—0
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions—0
QT-TDM: Planning With Transformer Dynamics Model and Autoregressive Q-Learning—0
Quadratic Q-network for Learning Continuous Control for Autonomous Vehicles—0
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping—0
Quantitative Trading using Deep Q Learning—0
Quantum Architecture Search via Continual Reinforcement Learning—0
Quantum deep Q learning with distributed prioritized experience replay—0
Quantum deep recurrent reinforcement learning—0
Quantum-Inspired Reinforcement Learning in the Presence of Epistemic Ambivalence—0
Quantum Observables for continuous control of the Quantum Approximate Optimization Algorithm via Reinforcement Learning—0
Deep Reinforcement Learning via L-BFGS Optimization—0
Q-WSL: Optimizing Goal-Conditioned RL with Weighted Supervised Learning via Dynamic Programming—0
Reward Prediction Error as an Exploration Objective in Deep RL—0
QXplore: Q-Learning Exploration by Maximizing Temporal Difference Error—0
Random-Key Algorithms for Optimizing Integrated Operating Room Scheduling—0
Rank-One Modified Value Iteration—0
RansomAI: AI-powered Ransomware for Stealthy Encryption—0
RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learning—0
Real-time Active Vision for a Humanoid Soccer Robot Using Deep Reinforcement Learning—0
Realtime Spectrum Monitoring via Reinforcement Learning -- A Comparison Between Q-Learning and Heuristic Methods—0
Real-World Offline Reinforcement Learning from Vision Language Model Feedback—0
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