  Group recommender systems are widely used in current web applications. In
this paper, we propose a novel group recommender system based on the deep
reinforcement learning. We introduce the MovieLens data at first and generate
one random group dataset, MovieLens-Rand, from it. This randomly generated
dataset is described and analyzed. We also present experimental settings and
two state-of-art baselines, AGREE and GroupIM. The framework of our novel
model, the Deep Reinforcement learning based Group Recommender system (DRGR),
is proposed. Actor-critic networks are implemented with the deep deterministic
policy gradient algorithm. The DRGR model is applied on the MovieLens-Rand
dataset with two baselines. Compared with baselines, we conclude that DRGR
performs better than GroupIM due to long interaction histories but worse than
AGREE because of the self-attention mechanism. We express advantages and
shortcomings of DRGR and also give future improvement directions at the end.
