Imitating Playstyle with Dynamic Time Warping Imitation

Imitation learning has been demonstrated as a useful technique in automatic game testing and the development of believable Non-Player Characters (NPCs). However, imitation learning methods typically focus on learning a policy to complete a task without consideration about the playstyle used. In this work we consider the case where the task is to imitate a given playstyle. We defined a player’s playstyle based on the strategies they use in order to complete the overall task. This has been achieved by rewarding a learning agent based on the similarity of the agent and demonstration trajectories, within a learnt representation space. This allows the playstyle to be learnt in levels that differ to the one the demonstrations were collected in.