  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.
