用强化学习控制机器人影响用户观点，提升广告投放效率
Towards Opinion Shaping: A Deep Reinforcement Learning Approach in Bot-User Interactions
- 用深度确定性策略梯度算法调控机器人位置与广告投放
- 实验显示可高效改变群体观点分布，效果优于传统方法
- 适合研究社交网络干预、数字营销的从业者和学者
本文研究通过用户-机器人互动对社交网络算法的干扰影响，聚焦随机有界信任模型（SBCM）。探讨两种策略：由智能体控制的机器人在网络中的定位，以及在不同条件下的定向广告投放，受广告预算约束。结合深度确定性策略梯度（DDPG）及其变体，开展多种深度强化学习（DRL）实验。实验结果表明，该方法可实现高效的舆论塑造，展现出在社交平台广告资源部署中的应用潜力。
原文摘要 · Abstract (English)
This paper aims to investigate the impact of interference in social network algorithms via user-bot interactions, focusing on the Stochastic Bounded Confidence Model (SBCM). This paper explores two approaches: positioning bots controlled by agents into the network and targeted advertising under various circumstances, operating with an advertising budget. This study integrates the Deep Deterministic Policy Gradient (DDPG) algorithm and its variants to experiment with different Deep Reinforcement Learning (DRL). Finally, experimental results demonstrate that this approach can result in efficient opinion shaping, indicating its potential in deploying advertising resources on social platforms.
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