We apply the techniques of deep reinforcement learning (RL) to the problem of coverage and capacity optimisation (CCO) in wireless networks. This is motivated by the idea that the type of combinatorial optimisation problems encountered in wireless networks are somewhat analogous to strategy games, for which deep RL has already proven to be an effective approach. We use a computer simulation of a small wireless network to generate synthetic data to train a deep Q network (DQN), and evaluate the performance of the DQN with further simulations. We compare the performance of the DQN with a conventional model-based approach. The results show that the DQN achieves slightly better performance than the conventional method, without the need for an explicit model of the environment. The performance is shown to be further improved by using the DQN within a search algorithm.