Value Iteration Solver Networks

Value Iteration Algorithm is iterative and can't be parallelized. Computation time grows exponentially when the size of the input maps is increased. We propose UNet-RNN-Skip artificial neural network architecture that can be used to parallelize Value Iteration Algorithm results. The proposed model can solve Value Iteration problem in fewer iterations than the original algorithm and computation time increases by only a small amount when increasing the size of the input map. Fundamental UNet-RNN-Skip architecture can be used also to solve and parallelize other sequential problems. With this paper synthetic dataset of maps and generator has been published to enable further studies in mapping and path planning tasks.