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This paper proposes a multi-agent reinforcement learning framework that co-trains an autonomous vehicle and pedestrians with personality-driven jaywalking behavior, achieving a 30% reduction in collisions compared to single-agent approaches and demonstrating more realistic interaction scenarios.