Tactical route planning in battlefield simulations with inverse reinforcement learning

Sammanfattning: In this report Deep Maximum Entropy Inverse Reinforcement Learning has been applied to the problem of route planning in rough terrain, while taking tactical parameters into account. The tactical parameters that the report focuses on is to avoid detection from predetermined static observers by keeping blocking terrain in the sight line. The experiments have been done in a simplified gridworld using generated "expert" routes in place of an actual expert. The purpose of the project is to serve as an indication that this algorithm can be used to model these types of problems. The results show that the algorithm indeed can approximate this type of function, but it remains to be proven that the methods are useful when examples are taken from an actual expert, and applied in a real world scenario. Another finding is that the choice of Value Iteration as the algorithm for calculating policies turns out to be very time consuming which limits the amount of training and the scope of the possible problem.