Generating competitive strategies and performing continuous motion planning simultaneously in an adversarial setting is a challenging problem. In addition, understanding the intent of other agents is crucial to deploying autonomous systems in adversarial multi-agent environments. Existing approaches either discretize agent action by grouping similar control inputs, sacrificing performance in motion planning, or plan in uninterpretable latent spaces, producing hard-to-understand agent behaviors. This paper proposes an agent strategy representation via Policy Characteristic Space that maps the agent policies to a pre-specified low-dimensional space. Policy Characteristic Space enables the discretization of agent policy switchings while preserving continuity in control. Also, it provides intepretability of agent policies and clear intentions of policy switchings. Then, regret-based game-theoretic approaches can be applied in the Policy Characteristic Space to obtain high performance in adversarial environments. Our proposed method is assessed by conducting experiments in an autonomous racing scenario using scaled vehicles. Statistical evidence shows that our method significantly improves the win rate of ego agent and the method also generalizes well to unseen environments.

翻译：在对抗性环境中同时生成竞争策略并执行连续运动规划是一个具有挑战性的问题。此外，理解其他智能体的意图对于在对抗性多智能体环境中部署自主系统至关重要。现有方法要么通过分组相似控制输入来离散化智能体动作（牺牲运动规划性能），要么在不可解释的潜空间中规划（导致难以理解的智能体行为）。本文提出了一种通过策略特征空间实现的智能体策略表示方法，该空间将智能体策略映射到预先指定的低维空间。策略特征空间能够在保持控制连续性的同时实现智能体策略切换的离散化。此外，它提供了智能体策略的可解释性以及策略切换的明确意图。随后，可在策略特征空间中应用基于遗憾的博弈论方法，以在对抗环境中获得高性能。我们通过使用缩比车辆在自主赛车场景中进行实验来评估所提方法。统计证据表明，我们的方法显著提高了自我智能体的胜率，并且该方法对未见过的环境也具有良好的泛化能力。