Reinforcement learning (RL) methods have been actively applied in the field of robotics, allowing the system itself to find a solution for a task otherwise requiring a complex decision-making algorithm. In this paper, we present a novel RL-based Tic-tac-toe scenario, i.e. SwarmPlay, where each playing component is presented by an individual drone that has its own mobility and swarm intelligence to win against a human player. Thus, the combination of challenging swarm strategy and human-drone collaboration aims to make the games with machines tangible and interactive. Although some research on AI for board games already exists, e.g., chess, the SwarmPlay technology has the potential to offer much more engagement and interaction with the user as it proposes a multi-agent swarm instead of a single interactive robot. We explore user's evaluation of RL-based swarm behavior in comparison with the game theory-based behavior. The preliminary user study revealed that participants were highly engaged 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 was analyzed and put under discussion. User study revealed that SwarmPlay has the potential to be implemented in a wider range of games, significantly improving human-drone interactivity.

中文翻译：

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SwarmPlay：由强化学习驱动的纳米无人机群的交互式井字棋盘游戏

强化学习 (RL) 方法已在机器人领域得到积极应用，允许系统本身为一项任务找到解决方案，否则需要复杂的决策算法。在本文中，我们提出了一种新颖的基于 RL 的井字游戏场景，即 SwarmPlay，其中每个播放组件都由一个单独的无人机呈现，该无人机具有自己的机动性和群体智能，可以战胜人类玩家。因此，具有挑战性的群体策略和人机协作的结合旨在使机器游戏具有可触性和交互性。虽然已经存在一些关于棋盘游戏人工智能的研究，例如国际象棋，但 SwarmPlay 技术有可能提供更多的用户参与和交互，因为它提出了一个多代理群而不是单个交互机器人。我们探索用户' s 评估基于 RL 的群体行为与基于博弈论的行为进行比较。初步用户研究显示，参与者高度参与了无人机游戏（70% 的人在李克特量表上给出了最高分），并且发现与常规的基于计算机的系统（80%）相比，它的人工程度更低。分析并讨论了用户游戏感知对其结果的影响。用户研究表明，SwarmPlay 有可能在更广泛的游戏中实施，显着提高人机交互性。初步用户研究显示，参与者高度参与了无人机游戏（70% 的人在李克特量表上给出了最高分），并且发现与常规的基于计算机的系统（80%）相比，它的人工程度更低。分析并讨论了用户游戏感知对其结果的影响。用户研究表明，SwarmPlay 有可能在更广泛的游戏中实施，显着提高人机交互性。初步用户研究显示，参与者高度参与了无人机游戏（70% 的人在李克特量表上给出了最高分），并且发现与常规的基于计算机的系统（80%）相比，它的人工程度更低。分析并讨论了用户游戏感知对其结果的影响。用户研究表明，SwarmPlay 有可能在更广泛的游戏中实施，显着提高人机交互性。