  Game development is a long process that involves many stages before a product
is ready for the market. Human play testing is among the most time consuming,
as testers are required to repeatedly perform tasks in the search for errors in
the code. Therefore, automated testing is seen as a key technology for the
gaming industry, as it would dramatically improve development costs and
efficiency. Toward this end, we propose EVOLUTE, a novel imitation
learning-based architecture that combines behavioural cloning (BC) with energy
based models (EBMs). EVOLUTE is a two-stream ensemble model that splits the
action space of autonomous agents into continuous and discrete tasks. The EBM
stream handles the continuous tasks, to have a more refined and adaptive
control, while the BC stream handles discrete actions, to ease training. We
evaluate the performance of EVOLUTE in a shooting-and-driving game, where the
agent is required to navigate and continuously identify targets to attack. The
proposed model has higher generalisation capabilities than standard BC
approaches, showing a wider range of behaviours and higher performances. Also,
EVOLUTE is easier to train than a pure end-to-end EBM model, as discrete tasks
can be quite sparse in the dataset and cause model training to explore a much
wider set of possible actions while training.

Machine Learning