VCMI is a new, open-source project that could become one of the biggest testing platform for modern AI algorithms in the future. Its complex environment and turn-based gameplay make it a perfect system for any AI driven solution. It also has a large community of active players which improves the testability of target algorithms. This paper explores VCMI’s environment and tries to assess its complexity by providing a base solution for battle handling problem using two global optimization algorithms: Co-Evolution of Genetic Programming Trees and TD(1) algorithm with Back Propagation neural network. Both algorithms have been used in VCMI to evolve battle strategies through a fully autonomous learning process. Finally, the obtained strategies have been tested against existing solutions and compared with players’ best tactics.
CITATION STYLE
Wilisowski, Ł., & Dreżewski, R. (2015). The application of co-evolutionary genetic programming and TD(1) reinforcement learning in large-scale strategy game VCMI. In Smart Innovation, Systems and Technologies (Vol. 38, pp. 81–93). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-319-19728-9_7
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