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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 441–450 of 1918 papers

TitleStatusHype
FRAC-Q-Learning: A Reinforcement Learning with Boredom Avoidance Processes for Social Robots—0
Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline Reinforcement Learning—0
Approximation of Convex Envelope Using Reinforcement Learning—0
Learning to Cooperate and Communicate Over Imperfect Channels—0
On optimal tracking portfolio in incomplete markets: The reinforcement learning approach—0
Efficient Open-world Reinforcement Learning via Knowledge Distillation and Autonomous Rule Discovery—0
Multi-intention Inverse Q-learning for Interpretable Behavior RepresentationCode0
Machine learning-based decentralized TDMA for VLC IoT networks—0
Decentralised Q-Learning for Multi-Agent Markov Decision Processes with a Satisfiability Criterion—0
Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets—0
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