Bridging the Gap between Discrete Agent Strategies in Game Theory and Continuous Motion Planning in Dynamic Environments

2024-03-17

arXiv.org

AI Summary

In adversarial multi-agent environments, existing approaches struggle to simultaneously achieve strategic competitiveness and motion continuity while lacking interpretability in intent understanding. Method: This paper proposes a novel paradigm that jointly optimizes discrete game-theoretic strategy selection and continuous motion planning. Its core innovation is the construction of a Policy Characteristic Space—a low-dimensional, interpretable latent space—enabling explicit modeling of strategic transitions. This design preserves the continuity of underlying control policies while ensuring human-readable behavioral intent. The framework integrates regret-minimization-based game solving for robust equilibrium computation. Results: Evaluated on a full-scale autonomous racing platform, the method significantly improves the ego-vehicle’s win rate against adversarial opponents and demonstrates strong generalization to unseen scenarios.

Technology Category

• Multiagent Systems: Adversarial Agents
• Game Theory and Economic Paradigms: Adversarial Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Combining game theory strategies with continuous motion planning
• Understanding agent intent in adversarial multi-agent environments
• Discretizing policy switches while maintaining control continuity

Innovation

• Policy Characteristic Space maps policies to low dimensions
• Combines discrete strategy switching with continuous control
• Uses regret-based game theory for adversarial performance