Shimon's research is primarily focused on single- and multi-agent decision-theoretic planning and learning, especially reinforcement learning, though he is also interested in stochastic optimization methods such as neuroevolution. Current research efforts include comparing disparate approaches to reinforcement learning, developing more rigorous frameworks for empirical evaluations, improving the scalability of multiagent planning, and applying learning methods to traffic management, helicopter control, and data filtering in high energy physics.