Contract bridge is a multi-player imperfect-information game where one partnership collaborate with each other to compete against the other partnership. The game consists of two phases: bidding and playing. While playing is relatively easy for modern software, bidding is challenging and requires agents to learn a communication protocol to reach the optimal contract jointly, with their own private information. The agents need to exchange information to their partners, and interfere opponents, through a sequence of actions. In this work, we train a strong agent to bid competitive bridge purely through selfplay, outperforming WBridge5, a championship-winning software. Furthermore, we show that explicitly modeling belief is not necessary in boosting the performance. To our knowledge, this is the first competitive bridge agent that is trained with no domain knowledge. It outperforms previous state-of-the-art that use human replays with 70x fewer number of parameters.
The authors propose a deep learning agent for automatic bidding in the bridge game. The agent is trained with a standard A3C reinforcement learning model with self-play, and the internal neural network only takes a rather succinct representation of the bidding history as the input. Experiment results demonstrate state-of-the-art performance with a simpler model. The authors discuss some findings with the proposed agent, such as the lack of need to explicitly model the belief and the possibility to self-train with different variants of opponents. Some visualization is also provided to understand how the trained agent behaves.
It is recommended to weak-reject the paper. There is an obvious contribution of using a simple model to reach state-of-the-art performance. The only concern is whether such a contribution is sufficient to warrant acceptance for a top conference. While setting a new milestone for the community is important (and having open-source code and data in the future will lead to a huge impact for the community), the current paper does not appear to have introduced sufficient "other contributions." In particular,
(1) The ablation study is very shallow. Section 4.2 shows four things: [a] training with an auxiliary belief task does not help the proposed agent; [b] simple representation is sufficiently good; [c] using a pool of opponents won't do better than using the most recent model as opponent; [d] frequency of updating the opponents does not matter much.
For [a], it is certainly interesting. But one could get to a deeper understanding by, for instance, zooming in to a ratio of 10^-3 or less, to see if there are sweet points of the choice of r. One could also get to a deeper understanding by checking how well BCELoss is doing, and how well the value loss is doing, to understand the trade-off. It is not even clear what the ranges of BCEloss and the value loss are, making it uncertain on whether r is properly chosen.
For [b], deeper study could be taken for analyzing "why", especially given that the more complicated encoding is significantly worse. The hand-waving explanation of "The potential reason why our encoding performs better is that the intrinsic order of bridge bidding is already kept by the action itself ..." at best suggests that the information between the baseline19 encoding and the author's are of the same information amount, but does not say why baseline19 is significantly worse. The authors leave a big question mark here that does not seem to match the grand title of "simple is better" of this paper.
For [c], deeper study could be taken for analyzing "what if some worse opponents are included" or "what if some targeted opponents are included." For instance, what if the authors include early-stage models to increase diversity? What is the trade-off between opponent capability/diversity and performance? What if using baseline16 as opponents---would the agent then beat baseline16 more easily? Note that baseline16 and the proposed agent basically learned a semi-natural bidding system. What if a precision system is set as a potential opponent---would the learned policy be very different?
For [d], getting a result that the update frequency leads to similar performance can hardly match the claim "We show that selfplay schedule and details are critical in learning imperfect information games." Also, the big error bar and the inconclusive trend in Table 1(B) does not answer the question on what frequency should best be used. Deeper discussions shall be included. 
(2) Besides the ablation study, there is very little information about the design choices. For instance, why does the authors choose the specific RFC block? Would a full FC block do better or worse? Would a shallower network do better or worse? Actually, the title "simple is better" may be misleading, as the authors did not study even simpler choices. For RL algorithm, why A3C but not other algorithms?
(3) The authors' finding that the agent is conservative and does not bid high appears interesting. Nevertheless, it perhaps suggests that the authors have not compared with strong bidding systems (like precision) that aimed for bidding high to win more. That is, the claimed advantages are over semi-natural bidding systems. More study on whether the agent should bid high could greatly enrich this paper.
===
I've read the rebuttal. I thank the authors for the discussion on some possibilities in deepening the paper. I suggest the authors to avoid some hand-waving claims such as "We argue it is easier to learn the connections between them." In general, some of the claims (such as "simple is better" in the title) lacks sufficient scientific support, and I believe that this paper will have more impacts if it focuses more on supporting the claims with science evidence, not with human descriptions.