weakness) The proposed approach is incremental relative to previous works.
Despite the novelty of the proposed approaches' application to learning communication protocols in MARL, these techniques have previously been proposed for other prediction problems, as alluded to by the authors in Lines 144 and 175. Line 155 also indicates that some theoretical contributions are minor extensions of theorems proven in previous work. Nevertheless, this weakness is outweighed by the remaining theoretical and empirical studies that the authors have conducted to show that the proposed approach can learn communication protocols requiring symmetry breaking and going beyond 1-WL expressivity in MARL. Quality
1. (Major Strength) Result reproducibility.
The authors have provided adequate descriptions of the model architecture and hyperparameters, environments, and experiment protocols that ensure their results are reproducible.
2. (Major Strength) The experiment design adequately demonstrates the claims made in by the authors.
The environment and baseline selection empirically highlight the claims of this work regarding RNI/CLIP's ability to learn improved communication protocols in environments requiring (i) symmetry-breaking or (ii) function estimators beyond the 1-WL expressivity. Note that the evaluation in Predator-Prey and Easy Traffic Junction also highlights the potential weaknesses of the proposed approach when applied to environments not requiring (i) or (ii). This provides valuable insights that can inform the readers when applying the proposed technique to other environments.
3. (Minor weakness) Missing theoretical/empirical analysis to strengthen the claims of this work further.
Despite ensuring the existence of a learned GNN that can help with (i) symmetry-breaking, I believe the theoretical analysis on the ability of RL-based optimisation techniques to learn such GNNs is lacking. Nonetheless, the empirical demonstration of RNI/CLIP's performance indicates that this is not a significant issue for the evaluated environments. Furthermore, since the work mentioned convergence as a potential issue with a few proposed approaches, it is crucial to report the learning curve from the experiments in the experiment section (although this is provided in the Appendix).
That aside, it will also be interesting to analyze messages that RNI/CLIP passes in environments requiring (i) or (ii). Specifically, in terms of proving the usefulness of RNI/CLIP in addressing (i), inspecting the passed representation may highlight how they aid the symmetry-breaking process. Clarity
1. (Strength) The paper is well-written.
The document is generally well-written. The method description and related theoretical analysis were clear and concise. Furthermore, the experiment section clearly states the intention behind the design/selection of various baseline/environments for demonstrating the central claims of the work. Significance
1. (Strength) The work provides significant results for people working in applying GNNs for learning communication protocols for MARL.
Despite the proposed approach mainly being incremental compared to previous work, the theoretical and empirical analysis provides findings useful for people working in communication for MARL. The way the authors highlighted the potential limitations of the work is specifically helpful for future research.
The authors have adequately highlighted the limitations of their work through their experiments and the resulting analysis. In terms of societal impact, I do not think that this work requires additional information regarding its negative societal impact since its application is still rather limited to simple MARL environments.
2. (Major Strength) The experiment design adequately demonstrates the claims made in by the authors. The environment and baseline selection empirically highlight the claims of this work regarding RNI/CLIP's ability to learn improved communication protocols in environments requiring (i) symmetry-breaking or (ii) function estimators beyond the 1-WL expressivity. Note that the evaluation in Predator-Prey and Easy Traffic Junction also highlights the potential weaknesses of the proposed approach when applied to environments not requiring (i) or (ii). This provides valuable insights that can inform the readers when applying the proposed technique to other environments.