# Learning Automata Based Q-learning for Content Placement in Cooperative Caching

arXiv: https://arxiv.org/abs/1903.06235 · HTML: https://mlsanity.com/paper/1903.06235/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2019.

## Built on by (most cited first, 2 of 2)

- [Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks](https://mlsanity.com/paper/2407.00286/) (2024)
- [Adaptive Learning with Artificial Barriers Yielding Nash Equilibria in General Games](https://mlsanity.com/paper/2203.15780/) (2022)

## Abstract (arXiv)

An optimization problem of content placement in cooperative caching is formulated, with the aim of maximizing sum mean opinion score (MOS) of mobile users. Firstly, a supervised feed-forward back-propagation connectionist model based neural network (SFBC-NN) is invoked for user mobility and content popularity prediction. More particularly, practical data collected from GPS-tracker app on smartphones is tackled to test the accuracy of mobility prediction. Then, a learning automata-based Q-learning (LAQL) algorithm for cooperative caching is proposed, in which learning automata (LA) is invoked for Q-learning to obtain an optimal action selection in a random and stationary environment. It is proven that the LA-based action selection scheme is capable of enabling every state to select the optimal action with arbitrarily high probability if Q-learning is able to converge to the optimal Q value eventually. To characterize the performance of the proposed algorithms, the sum MOS of users is applied to define the reward function. Extensive simulations reveal that: 1) The prediction error of SFBC-NN lessen with the increase of iterations and nodes; 2) the proposed LAQL achieves significant performance improvement against traditional Q-learning; 3) the cooperative caching scheme is capable of outperforming non-cooperative caching and random caching of 3% and 4%.