# Fast Reinforcement Learning for Anti-jamming Communications

arXiv: https://arxiv.org/abs/2002.05364 · HTML: https://mlsanity.com/paper/2002.05364/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [Multi-Agent Adversarial Attacks for Multi-Channel Communications](https://mlsanity.com/paper/2201.09149/) (2022)

## Abstract (arXiv)

This letter presents a fast reinforcement learning algorithm for anti-jamming communications which chooses previous action with probability τ and applies ε-greedy with probability (1-τ). A dynamic threshold based on the average value of previous several actions is designed and probability τ is formulated as a Gaussian-like function to guide the wireless devices. As a concrete example, the proposed algorithm is implemented in a wireless communication system against multiple jammers. Experimental results demonstrate that the proposed algorithm exceeds Q-learing, deep Q-networks (DQN), double DQN (DDQN), and prioritized experience reply based DDQN (PDDQN), in terms of signal-to-interference-plus-noise ratio and convergence rate.