# A Reinforcement Learning Approach for the Multichannel Rendezvous Problem

arXiv: https://arxiv.org/abs/1907.01919 · HTML: https://mlsanity.com/paper/1907.01919/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

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

- [Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning](https://mlsanity.com/paper/2101.00687/) (2021)

## Abstract (arXiv)

In this paper, we consider the multichannel rendezvous problem in cognitive radio networks (CRNs) where the probability that two users hopping on the same channel have a successful rendezvous is a function of channel states. The channel states are modelled by two-state Markov chains that have a good state and a bad state. These channel states are not observable by the users. For such a multichannel rendezvous problem, we are interested in finding the optimal policy to minimize the expected time-to-rendezvous (ETTR) among the class of em dynamic blind rendezvous policies, i.e., at the tth time slot each user selects channel i independently with probability pi(t), i=1,2, ldots, N. By formulating such a multichannel rendezvous problem as an adversarial bandit problem, we propose using a reinforcement learning approach to learn the channel selection probabilities pi(t), i=1,2, ldots, N. Our experimental results show that the reinforcement learning approach is very effective and yields comparable ETTRs when comparing to various approximation policies in the literature.