Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks
Learning when to communicate and doing that effectively is essential in multi-agent tasks. Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks. In this paper, we present Individualized Controlled Continuous Communication Model (IC3Net) which has better training efficiency than simple continuous communication model, and can be applied to semi-cooperative and competitive settings along with the cooperative settings. IC3Net controls continuous communication with a gating mechanism and uses individualized rewards foreach agent to gain better performance and scalability while fixing credit assignment issues. Using variety of tasks including StarCraft BroodWars explore and combat scenarios, we show that our network yields improved performance and convergence rates than the baselines as the scale increases. Our results convey that IC3Net agents learn when to communicate based on the scenario and profitability.
 From a methodological perspective, this paper describes a simple bu clever learning architecture with individual agents able to decide when to communicate through a learned gating mechanism. Each agent is an LSTM able to decide at each time point which aspects of its internal state should be exposed to other agents through this gating mechanism. The presentation of this method is clear to a level that should allows the reader to implement this him/herself. It would be great if the code associated to this could be released but the presentation allows for reproducibility. The experiments are interesting as well. Experimental results are presented on 3 problems and compared with known baselines from the academic community. The obtained results do show the merit of the approach. That being said, while the experimental results are extensive, there are places that could benefit from more clarity. For instance, I have found section 4.2 a bit dry. For instance, I had to read the plots caption and the text several times to map get at the deductions made in 4.2. Given the importance of gating in this work, I recommend expanding on this a bit (if space allows it). Small note: in the caption for Figure 3, on the fourth line, did you mean (f) instead of (d) when arguing that agents stop communicating once they reach the prey ( or am I missing something here)? Also, would it be possible to provide more insights on why IC3Net is doing better than CommNet except for the Combat-10Mv3Ze task (last table before the conclusion, what makes this task harder for IC3Net)? Another observation is on the variance terms that are reported for IC3Net. They are often (not always but definitely in the last table before the conclusion) quite higher when compared to the values associated with the baselines. Can this be explained? Another small thing: please add captions to your tables (at least a table number; I think that Table 2 does not have a caption). Overall, the paper is well written, interesting. Addressing the questions raised above would definitely help me and probably the eventual readers better appreciate its quality.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks
Learning when to communicate and doing that effectively is essential in multi-agent tasks. Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks. In this paper, we present Individualized Controlled Continuous Communication Model (IC3Net) which has better training efficiency than simple continuous communication model, and can be applied to semi-cooperative and competitive settings along with the cooperative settings. IC3Net controls continuous communication with a gating mechanism and uses individualized rewards foreach agent to gain better performance and scalability while fixing credit assignment issues. Using variety of tasks including StarCraft BroodWars explore and combat scenarios, we show that our network yields improved performance and convergence rates than the baselines as the scale increases. Our results convey that IC3Net agents learn when to communicate based on the scenario and profitability.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 From a methodological perspective, this paper describes a simple bu clever learning architecture with individual agents able to decide when to communicate through a learned gating mechanism. Each agent is an LSTM able to decide at each time point which aspects of its internal state should be exposed to other agents through this gating mechanism. The presentation of this method is clear to a level that should allows the reader to implement this him/herself. It would be great if the code associated to this could be released but the presentation allows for reproducibility. The experiments are interesting as well. Experimental results are presented on 3 problems and compared with known baselines from the academic community. The obtained results do show the merit of the approach. That being said, while the experimental results are extensive, there are places that could benefit from more clarity. For instance, I have found section 4.2 a bit dry. For instance, I had to read the plots caption and the text several times to map get at the deductions made in 4.2. Given the importance of gating in this work, I recommend expanding on this a bit (if space allows it). Small note: in the caption for Figure 3, on the fourth line, did you mean (f) instead of (d) when arguing that agents stop communicating once they reach the prey ( or am I missing something here)? Also, would it be possible to provide more insights on why IC3Net is doing better than CommNet except for the Combat-10Mv3Ze task (last table before the conclusion, what makes this task harder for IC3Net)? Another observation is on the variance terms that are reported for IC3Net. They are often (not always but definitely in the last table before the conclusion) quite higher when compared to the values associated with the baselines. Can this be explained? Another small thing: please add captions to your tables (at least a table number; I think that Table 2 does not have a caption). Overall, the paper is well written, interesting. Addressing the questions raised above would definitely help me and probably the eventual readers better appreciate its quality.
assistant
 From a methodological perspective, this paper describes a simple bu clever learning architecture with individual agents able to decide when to communicate through a learned gating mechanism. Each agent is an LSTM able to decide at each time point which aspects of its internal state should be exposed to other agents through this gating mechanism. The presentation of this method is clear to a level that should allows the reader to implement this him/herself. It would be great if the code associated to this could be released but the presentation allows for reproducibility. The experiments are interesting as well. Experimental results are presented on 3 problems and compared with known baselines from the academic community. The obtained results do show the merit of the approach. That being said, while the experimental results are extensive, there are places that could benefit from more clarity. For instance, I have found section 4.2 a bit dry. For instance, I had to read the plots caption and the text several times to map get at the deductions made in 4.2. Given the importance of gating in this work, I recommend expanding on this a bit (if space allows it). Small note: in the caption for Figure 3, on the fourth line, did you mean (f) instead of (d) when arguing that agents stop communicating once they reach the prey ( or am I missing something here)? Also, would it be possible to provide more insights on why IC3Net is doing better than CommNet except for the Combat-10Mv3Ze task (last table before the conclusion, what makes this task harder for IC3Net)? Another observation is on the variance terms that are reported for IC3Net. They are often (not always but definitely in the last table before the conclusion) quite higher when compared to the values associated with the baselines. Can this be explained? Another small thing: please add captions to your tables (at least a table number; I think that Table 2 does not have a caption). Overall, the paper is well written, interesting. Addressing the questions raised above would definitely help me and probably the eventual readers better appreciate its quality.