We apply an adapted version of Particle Swarm Optimization to distributed unsupervised robotic learning in groups of robots with only local information. The performance of the learning technique for a simple task is compared across robot groups of various sizes, with the maximum group size allowing each robot to individually contain and manage a single PSO particle. Different PSO neighborhoods based on limitations of real robotic communication are tested in this scenario, and the effect of varying communication power is explored. The algorithms are then applied to a group learning scenario to explore their susceptibility to the credit assignment problem. Results are discussed and future work is proposed.