Paper:
This paper introduces the deep coordination graph (DCG) for collaborative multiagent reinforcement learning. DCG strikes a flexible trade-off between representational capacity and generalization by factorizing the joint value function of all agents according to a coordination graph into payoffs between pairs of agents. The value can be maximized by local message passing along the graph, which allows training of the value function end-to-end with Q-learning. Payoff functions are approximated with deep neural networks and parameter sharing improves generalization over the state-action space. We show that DCG can solve challenging predator-prey tasks that are vulnerable to the relative overgeneralization pathology and in which all other known value factorization approaches fail.

Review:
This paper introduces the deep coordination graph for collaborative multi-agent reinforcement learning aimed to solve predator-prey tasks by preventing relative overgeneralization during the exploration of agents. 
In general, this paper gives a detailed and comprehensible depiction of the Introduction, Related work and Background section. However, I have two concerns about their method and experiments. 
The presentation of the “Method” section is not clear enough to evaluate the contribution of this paper. Specifically: 
(1). In the "Method" section, your method incorporates three ideas: 1. restricting the payoffs; 2. sharing parameters; 3. allowing generalization; 
In my understanding, both idea 1 and idea 2 come from VDN [4]. Idea 3 is not implemented in this work. Then, what is your contribution?
(2). According to the announcement, the key benefit for your work is "prevent relative overgeneralization during exploration of agents". It is hard to access if the proposed method can prevent overgeneralization. Do you have some theoretical or empirical justifications?
(3). I think it would be helpful if you could give a description according to your algorithm in the appendix. It would be better to draw a diagram to show what is your model.
I think the experiments are too simple and not convincing. 
(1). I notice recent multi-agent reinforcement papers ([1], [2] closely related to your work) evaluate their work on the challenging set of StarCraft II micromanagement tasks and achieved the evident result. 
 
(2). In fig.2, why did you only compare your model with VDN [4]?
(3). In fig.3, why does the return value of QTRAN first decrease and then increase? From [3], it seems their return curve continuously grows. 
(4). In fig.4, why QTRAN fail to this task?
[1]. Rashid, Tabish, et al. "QMIX: monotonic value function factorisation for deep multi-agent reinforcement learning." arXiv preprint arXiv:1803.11485 (2018).
[2]. Wang, Tonghan, et al. "Learning Nearly Decomposable Value Functions Via Communication Minimization." arXiv preprint arXiv:1910.05366 (2019).
[3]. Son, Kyunghwan, et al. "QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning." arXiv preprint arXiv:1905.05408 (2019).
[4]. Sunehag, Peter, et al. "Value-decomposition networks for cooperative multi-agent learning." arXiv preprint arXiv:1706.05296 (2017).