Introduction

This collection should include: - The concept of a policy - Learning using the simulation itself (evolutionary strategies, monte carlo tree search and touch on how these are different to Deep RL but still in the same family of sample-driven - which depend on simulations existing) - Linear/Dynamic/Stochastic programming (all examples of model-driven - which don’t use a simulation but instead a pre-existing model) - How the simulation can be used to test all of these policies anyway - Refer to Warren Powell’s work and “The Decision Factory” - Collect examples of decision-making domains where this automation is more possible/feasible than others due to action-taking cadence, regulatory requirements, ethical issues that are highly relevant to the problem and other situations - AI ethics potentially worth touching on as well