# PersuasionRL

- **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:** [Araav Nayak](https://cerebralvalley.ai/u/Rav007)
- **GitHub:** https://github.com/AraavNayak/Persuasion-Agent-RL-Environment
- **Website:** https://colab.research.google.com/drive/1jtIIV-5FhYXVYpbqf6x7U_X-Nctvvgzu#scrollTo=XbjZe4OKA0v2
- **Demo video:** https://youtu.be/VwAnedG42Fw
- **Hugging Face:** https://huggingface.co/spaces/Araav/PersuasionRL
- **Gallery:** https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery/98

My project is about Adaptive Multi-Agent Deception: Can AI Learn to Sell to Adversarial Personalities?
The Challenge: The prospect is adversarial. it has hidden traits (price_sensitivity, relationship_importance, decision_speed) that determine whether sales rep's actions work or backfire. The sales agent must learn to:
* Infer hidden state (is this prospect relationship-driven or transactional?)
* Adapt strategy mid-episode
* Generalize across personalities (learn different policies for 8+ prospect archetypes)
Novel Contribution: Closed-loop LLM oversight that analyzes failure modes and writes executable policy hints, creating a human-AI-RL feedback loop.

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Markdown version of https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery/98. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt