Biddy Bears

Urban traffic congestion degrades commute times, increases emissions, and strains infrastructure, primarily because fixed-cycle traffic lights can't adapt to real-time conditions. Agentic Traffic combines deep reinforcement learning with a fine-tuned LLM to control intersections across procedurally generated city networks. A dueling Double DQN with prioritized replay learns phase-selection across diverse scenarios (rush hour, accidents, construction), augmented by a fine-tuned Llama 3.1 8B model that guides action selection through contextual reasoning and awareness of surrounding signals. The hybrid system achieves ~30% improvement over the standalone DQN baseline in wait times and throughput, outperforming fixed-cycle and random baselines by a wider margin. The project includes CityFlow, a simulation environment with district-aware policy variants and a multi-policy comparison dashboard for visualizing replay behavior side-by-side.