Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: In this blog post, the authors have described the problem of using reinforcement learning (RL) to control power grids. Specifically, they describe the L2RPN competition which was one by the algorithm "SMAAC"

I find the overall topic of the blog very appealing. It is always nice to be introduced to new problem settings, and to understand some of the intricacies involved of deploying AI solutions in these settings. I think the strength of the post is the introduction to the problem of controlling power systems and the description of the competition. 

I would have appreciated more detail about the SMAAC approach that won the competition. In particular, the "afterstate" method sounds intriguing but is not really explained. It might have also been nice to give an overview of what some of the other competitors had tried, and why the SMAAC method was superior. 

Another interesting consideration to include in L2RPN competition could be "demand-side management", which is becoming somewhat more common. In this setting, the operator also has some control over the load, particularly specific loads that have some inherent energy storage component, such as air condidtioners or water heaters. 

In summary, the topic is niche but of interest, but the post falls a little bit short in terms of describing detailed methods or challenges specific to RL. 
review_title: Official Review

final_score: rating: 5: Marginally below acceptance threshold
review_content: **overview** :

In the blog, the author illustrates the RL environment for  power systems and discusses issues overlooked in the current environment. In order to make it realistic for application in the real world, the author proposes two suggestions, the agent action and the reward function. 

**pros** :

* The topic that the blog covers  (AI for the power grid management) is very crucial these days due to the climate change, and thereby this blog can motivate other AI researchers to work on this important problem. 
* The blog is well-written and easy to follow even for non-experts in the power grid. The problem setting and its limitation are explained well with clear motivation.
* The proposed suggestions seem reasonable considering application in the real world. 

**cons** :

* There is not enough significant added value in comparison to the cited papers. The major content of the blog is about the simulator that the cited paper uses rather than the method and contribution of the cited ICLR 2021 paper. Therefore, I'm not sure if it fits the spirit of the blogpost track.
* As far as I know, some of the proposed suggestion about the generation dispatch (generator maximum capacity, minimum capacity and ramp up and ramp down time) is already implemented in the latest version. Furthermore,  L2RPN does not consider all generator types as dispatchable units. They explains well about the generation dispatch action in 
https://github.com/rte-france/Grid2Op/blob/1f676449b880c78361c530af10a1c9c797b77d90/getting_started/06_Redispatching_Curtailment.ipynb
* More detailed explanation about the reward function (e.g. what each variable means and how it comes out)  is needed for better understanding. 
review_title: Review

Decision: Reject