# Biddy Bears

- **Event:** [OpenEnv Hackathon SF](https://cerebralvalley.ai/e/openenv-hackathon-sf)
- **When:** Mar 7 at 9:00 AM – Mar 8 at 12:00 AM (PST)
- **Where:** Shack15, San Francisco, CA
- **Team:** [Aditya Mangalampalli](https://cerebralvalley.ai/u/Aditya2162), [Kevin Truong](https://cerebralvalley.ai/u/toke)
- **GitHub:** https://github.com/amangalampalli/agentic-traffic
- **Website:** https://colab.research.google.com/drive/168jPKWMi1Hyhnlb3LbV6DzSbIt9r6Vt6
- **Demo video:** https://www.youtube.com/watch?v=GEbvoKD4oho
- **Hugging Face:** https://huggingface.co/spaces/tokev/traffic-visualizer
- **Gallery:** https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery/12

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.

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Markdown version of https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery/12. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt