Recently, deep neural networks have achieved superhuman performance in various games such as Go, chess and Shogi. Compared to Go, China Competitive Poker, also known as Dou dizhu, is a type of imperfect information game, including hidden information, randomness, multi-agent cooperation and competition. It has become widespread and is now a national game in China. We introduce an approach to play China Competitive Poker using Convolutional Neural Network (CNN) to predict actions. This network is trained by supervised learning from human game records. Without any search, the network already beats the best AI program by a large margin, and also beats the best human amateur players in duplicate mode.
This paper provides a system to play CCP using some deep learning. The system consists of  three modules - the bid module, which is rule based, and the policy and kicker networks, which are simple convolutional neural networks. The authors use a dataset of 8 million game records consisting of 80 million state action pairs, and train the network in a supervised fashion. The resulting model is able beat MicroWe, the current state of the art in playing CCP, and even are able to beat a few "top amateur players"
- Why is the bid module also not learned? It seems like the feature set for the bid module is fairly simple, and a linear or MLP can do fairly well compared to a rule based module.
- It's not clear that separating the policy and kicker networks would be more advantageous than combining them. Thousands of actions is not a too large number - language modeling work routinely deals with outputting many more classes than that.
- Were the convolutions chosen 1D, 2D, or 3D? The figure seems to imply that the convolutions were over the XZ dimensions, with Y as the channel dimension. If so, this doesn't make too much sense to be, since the Z dimension is not uniform - the last index is all unseen cards, which is significantly more than the middle indices of "what was played in this round". There shouldn't be a lot of translational invariance in the Z dimension. I'm also not convinced that translational invariance is helpful in the X dimension.
- There were no comparisons with baseline models or different model architectures. I would like to see some results on the same structure, but with an Linear model, MLP or LSTM across the time dimension, or search through different types of convolutional networks.
- What hyperparameters were searched through in the learning process?
- Missing citations for MicroWe being the best CCP AI, and citations for the accomplishments of the top amateur players.
- How far away are the top amateur players from professional players? Please provide some context on how far this system is from solving CCP.
- Fig 3, 4 should just say #of games instead of "iteration"
This paper shows that one choice for a supervised learning system on a CCP game database can achieve amateur level human play. It does not give insight to why the system was designed this way, why the model choices were made, and how good simpler baselines might be able to achieve. The paper is not clearly written enough, and does not provide enough scientific value to be accepted to the conference.