text: The authors study the problem of distributed routing in a network, where the goal is to minimize the maximal load (i.e. the load of the link with the highest utilization).  The authors advocate to use multi-agent reinforcement learning.  The main idea put forward by the authors is that by designing artificial rewards (to guide the agents), one can achieve faster exploration, in order to reduce convergence time. While the authors put forward several interesting ideas,  there are some shortcomings to the present version of the paper, including: - The design objective seems flawed from the networking point of view:  while minimizing the maximal load of a link is certainly a good starting point (to avoid instable queues)  one typically wants to minimize delay (or maximize flow throughput).  Indeed, it is possible to have a larger maximal load while reducing delay in many cases. - Furthermore, the authors do not provide a baseline to which the outcome of the learning algorithms they propose:  for instance how does their approach compare to simple policies (those are commonplace in networking) such as MaxWeight, Backpressure and so on ? - The authors argue that using multi-agent learning is more desirable than single agent (i.e. with a single reward signal which is common to all agents).  However, is multi-agent guaranteed to converge in such a setting ?  If some versions of the problem (for some particular reward signal) are not guaranteed to converge, it is difficult to understand whether "convergence"  is slow due to an inefficient exploration, or simply because convergence cannot occur in the first place. - The learning algorithms used are not clearly explained:  the authors simply state that they use "ACCNet" (from some unpublished prior work),  but to readers unfamiliar with this algorithm, it is difficult to judge the contents of the paper.  - In the numerical experiments, what is the "convergence rate" ?  is it the ratio between the mean reward of the learnt policy and that of the optimal ?  For how many time steps are the learning algorithm run before evaluating their outcome ?  What are the meaning of the various input parameter of ACCnet,  and is the performance sensitive to those parameters ?