  Visible Light Communication (VLC) has been widely investigated during the
last decade due to its ability to provide high data rates with low power
consumption. In general, resource management is an important issue in cellular
networks that can highly effect their performance. In this paper, an
optimisation problem is formulated to assign each user to an optimal access
point and a wavelength at a given time. This problem can be solved using mixed
integer linear programming (MILP). However, using MILP is not considered a
practical solution due to its complexity and memory requirements. In addition,
accurate information must be provided to perform the resource allocation.
Therefore, the optimisation problem is reformulated using reinforcement
learning (RL), which has recently received tremendous interest due to its
ability to interact with any environment without prior knowledge. In this
paper, we investigate solving the resource allocation optimisation problem in
VLC systems using the basic Q-learning algorithm. Two scenarios are simulated
to compare the results with the previously proposed MILP model. The results
demonstrate the ability of the Q-learning algorithm to provide optimal
solutions close to the MILP model without prior knowledge of the system.
