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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 1251–1260 of 1918 papers

TitleStatusHype
GenCos' Behaviors Modeling Based on Q Learning Improved by Dichotomy—0
Cooperative Control of Mobile Robots with Stackelberg Learning—0
QPLEX: Duplex Dueling Multi-Agent Q-LearningCode1
Momentum Q-learning with Finite-Sample Convergence Guarantee—0
Deep Reinforcement Learning for Dynamic Spectrum Sensing and Aggregation in Multi-Channel Wireless Networks—0
Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient—0
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures—0
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL—0
Trade-off on Sim2Real Learning: Real-world Learning Faster than Simulations—0
A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks—0
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