StagePilot: A Deep Reinforcement Learning Agent for Stage-Controlled Cybergrooming Simulation
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911422283251712 |
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| author | An, Heajun Zhang, Qi Liu, Minqian Zhang, Xinyi Lee, Sang Won Huang, Lifu Wisniewski, Pamela J. Cho, Jin-Hee |
| author_facet | An, Heajun Zhang, Qi Liu, Minqian Zhang, Xinyi Lee, Sang Won Huang, Lifu Wisniewski, Pamela J. Cho, Jin-Hee |
| contents | Cybergrooming is an evolving threat to youth, necessitating proactive educational interventions. We propose StagePilot, an offline RL-based dialogue agent that simulates the stage-wise progression of grooming behaviors for prevention training. StagePilot selects conversational stages using a composite reward that balances user sentiment and goal proximity, with transitions constrained to adjacent stages for realism and interpretability. We evaluate StagePilot through LLM-based simulations, measuring stage completion, dialogue efficiency, and emotional engagement. Results show that StagePilot generates realistic and coherent conversations aligned with grooming dynamics. Among tested methods, the IQL+AWAC agent achieves the best balance between strategic planning and emotional coherence, reaching the final stage up to 43% more frequently than baselines while maintaining over 70% sentiment alignment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_05060 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | StagePilot: A Deep Reinforcement Learning Agent for Stage-Controlled Cybergrooming Simulation An, Heajun Zhang, Qi Liu, Minqian Zhang, Xinyi Lee, Sang Won Huang, Lifu Wisniewski, Pamela J. Cho, Jin-Hee Machine Learning Computation and Language Cybergrooming is an evolving threat to youth, necessitating proactive educational interventions. We propose StagePilot, an offline RL-based dialogue agent that simulates the stage-wise progression of grooming behaviors for prevention training. StagePilot selects conversational stages using a composite reward that balances user sentiment and goal proximity, with transitions constrained to adjacent stages for realism and interpretability. We evaluate StagePilot through LLM-based simulations, measuring stage completion, dialogue efficiency, and emotional engagement. Results show that StagePilot generates realistic and coherent conversations aligned with grooming dynamics. Among tested methods, the IQL+AWAC agent achieves the best balance between strategic planning and emotional coherence, reaching the final stage up to 43% more frequently than baselines while maintaining over 70% sentiment alignment. |
| title | StagePilot: A Deep Reinforcement Learning Agent for Stage-Controlled Cybergrooming Simulation |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2602.05060 |