A model-free reinforcement technique in machine learning and data mining that compares available actions to states of the expected actions for a given machine state. Q-learning is able to find the optimal set of states for any given finite Markov decision process (MDP).

### Timeline

### People

### Further reading

A Deep Q-Learning Agent for the L-Game with Variable Batch Training

Petros Giannakopoulos, Yannis Cotronis

Academic paper

A Simple Reinforcement Learning Mechanism for Resource Allocation in LTE-A Networks with Markov Decision Process and Q-Learning

Einar Cesar Santos

Academic paper

Balancing Two-Player Stochastic Games with Soft Q-Learning

Jordi Grau-Moya, Felix Leibfried, Haitham Bou-Ammar

Academic paper

Bayesian Q-learning with Assumed Density Filtering

Heejin Jeong, Daniel D. Lee

Academic paper

Combining policy gradient and Q-learning

Brendan O'Donoghue, Remi Munos, Koray Kavukcuoglu, Volodymyr Mnih

Academic paper

Continuous Deep Q-Learning with Model-based Acceleration

Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, Sergey Levine

Academic paper

Distributed Deep Q-Learning

Hao Yi Ong, Kevin Chavez, Augustus Hong

Academic paper

Faster Deep Q-learning using Neural Episodic Control

Daichi Nishio, Satoshi Yamane

Academic paper

Monte Carlo Q-learning for General Game Playing

Hui Wang, Michael Emmerich, Aske Plaat

Academic paper

Multi-Agent Q-Learning for Minimizing Demand-Supply Power Deficit in Microgrids

Raghuram Bharadwaj Diddigi, D. Sai Koti Reddy, Shalabh Bhatnagar

Academic paper

Natural Gradient Deep Q-learning

Ethan Knight, Osher Lerner

Academic paper

Neurohex: A Deep Q-learning Hex Agent

Kenny Young, Ryan Hayward, Gautham Vasan

Academic paper

Q-learning optimization in a multi-agents system for image segmentation

Issam Qaffou, Mohamed Sadgal, Abdelaziz Elfazziki

Academic paper

Q-Learning with Basic Emotions

Wilfredo Badoy Jr., Kardi Teknomo

Academic paper

Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution

Pooyan Jamshidi, Amir Sharifloo, Claus Pahl, Andreas Metzger, Giovani Estrada

Academic paper

Using deep Q-learning to understand the tax evasion behavior of risk-averse firms

Nikolaos D. Goumagias, Dimitrios Hristu-Varsakelis, Yannis M. Assael

Academic paper