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

Currently, no **events** have been added to this timeline yet.

Be the first one to add some.

## People

## Further reading

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

Combining policy gradient and Q-learning

Academic paper

Daichi Nishio, Satoshi Yamane

Faster Deep Q-learning using Neural Episodic Control

Academic paper

Einar Cesar Santos

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

Academic paper

Ethan Knight, Osher Lerner

Natural Gradient Deep Q-learning

Academic paper

Hao Yi Ong, Kevin Chavez, Augustus Hong

Distributed Deep Q-Learning

Academic paper

Heejin Jeong, Daniel D. Lee

Bayesian Q-learning with Assumed Density Filtering

Academic paper

Hui Wang, Michael Emmerich, Aske Plaat

Monte Carlo Q-learning for General Game Playing

Academic paper

Issam Qaffou, Mohamed Sadgal, Abdelaziz Elfazziki

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

Academic paper

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

Balancing Two-Player Stochastic Games with Soft Q-Learning

Academic paper

Kenny Young, Ryan Hayward, Gautham Vasan

Neurohex: A Deep Q-learning Hex Agent

Academic paper

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

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

Academic paper

Petros Giannakopoulos, Yannis Cotronis

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

Academic paper

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

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

Academic paper

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

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

Academic paper

Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, Sergey Levine

Continuous Deep Q-Learning with Model-based Acceleration

Academic paper

Wilfredo Badoy Jr., Kardi Teknomo

Q-Learning with Basic Emotions

Academic paper

## Documentaries, videos and podcasts

## Companies