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Reinforcement learning (RL) methods have been actively applied in the fieldof robotics, allowing the system itself to find a solution for a task otherwiserequiring a complex decision-making algorithm. In this paper, we present anovel RL-based Tic-tac-toe scenario, i.e. SwarmPlay, where each playingcomponent is presented by an individual drone that has its own mobility andswarm intelligence to win against a human player. Thus, the combination ofchallenging swarm strategy and human-drone collaboration aims to make the gameswith machines tangible and interactive. Although some research on AI for boardgames already exists, e.g., chess, the SwarmPlay technology has the potentialto offer much more engagement and interaction with the user as it proposes amulti-agent swarm instead of a single interactive robot. We explore user'sevaluation of RL-based swarm behavior in comparison with the game theory-basedbehavior. The preliminary user study revealed that participants were highlyengaged in the game with drones (70% put a maximum score on the Likert scale)and found it less artificial compared to the regular computer-based systems(80%). The affection of the user's game perception from its outcome wasanalyzed and put under discussion. User study revealed that SwarmPlay has thepotential to be implemented in a wider range of games, significantly improvinghuman-drone interactivity.

SwarmPlay: Interactive Tic-tac-toe Board Game with Swarm of Nano-UAVs driven by Reinforcement Learning