We introduce an online popularity prediction and tracking task as a benchmark task for reinforcement learning with a combinatorial, natural language action space. A specified number of discussion threads predicted to be popular are recommended, chosen from a fixed window of recent comments to track. Novel deep reinforcement learning architectures are studied for effective modeling of the value function associated with actions comprised of interdependent sub-actions. The proposed model, which represents dependence between sub-actions through a bi-directional LSTM, gives the best performance across different experimental configurations and domains, and it also generalizes well with varying numbers of recommendation requests.
@article{arxiv.1606.03667,
title = {Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads},
author = {Ji He and Mari Ostendorf and Xiaodong He and Jianshu Chen and Jianfeng Gao and Lihong Li and Li Deng},
journal= {arXiv preprint arXiv:1606.03667},
year = {2016}
}