Reinforcement learning has seen major success in games and other artificial environments, but its applications in industries and real life are still limited. This limited applicability is partly due to the requirement of the large amount of the training data that needs to be collected through trial and error as well as the difficulty in effectively dealing with multiple or many agents. Diversity and negative dependence are a promising approach to resolve some of the major challenges in today’s reinforcement learning and have gained increasing attention in recent years. In this talk, we will briefly review some of the approaches to introducing diversity in reinforcement learning with a focus on the use of determinantal point processes for effective multi-agent reinforcement learning.