Game-theoretic Objective Space Planning
This addresses the challenge of deploying autonomous systems in adversarial scenarios by enhancing interpretability and performance, though it appears incremental as it builds on existing game-theoretic and planning methods.
The paper tackles the problem of generating competitive strategies and continuous motion planning in adversarial multi-agent environments by proposing a framework with agent action discretization via abstraction, offline population synthesis, and planning using counterfactual regret minimization. The result is improved learning and win rates against opponents, with transferability to unseen settings.
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. Furthermore, the most popular policy optimization frameworks do not recognize the long-term effect of actions and become myopic. This paper proposes an agent action discretization method via abstraction that provides clear intentions of agent actions, an efficient offline pipeline of agent population synthesis, and a planning strategy using counterfactual regret minimization with function approximation. Finally, we experimentally validate our findings on scaled autonomous vehicles in a head-to-head racing setting. We demonstrate that using the proposed framework significantly improves learning, improves the win rate against different opponents, and the improvements can be transferred to unseen opponents in an unseen environment.