Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 4: Ok but not good enough - rejection
review_content: **Is there a significant added value in comparison to the cited papers?**

No. The major concern is that this blog is closely related to the ICML 2018 paper (i.e., QMIX), not the cited ICLR paper (QPLEX). In the context of this blog, the cited paper is barely mentioned. It is unclear to distinguish the added value compared to the cited papers.

**Is this added value supported by accurate, convincing, and clear arguments?**

No. The reviewer is confused by this blog's description of QPLEX, i.e., "QPLEX transfers the monotonicity constraint from Q values to Advantage values, and introduces a duplex transformed network to integrate the state information." Actually, the cited paper (QPELX) transfers the **IGM** constraint from Q values to advantage values, and introduces a duplex transformed network to integrate the **joint state and action** information. Note that monotonicity constraint is a sufficient but not necessary condition for IGM constraint, which has been studied in several papers [1]. Integrating information about joint action into the hypernet architecture is the major contribution of QPLEX, which is different from other structures such as VDN and QMIX. This blog makes use of several implementation tricks to improve the performance of QMIX, but it is unclear how well they perform on the cited paper (QPLEX).

**STRENGTHS**

This blog is well-written and easy to follow. This blog conducts sufficient ablation studies to demonstrate several useful implementation tricks on QMIX. Experiments are evaluated on a variety of challenging settings.

**WEAKNESSES**

As discussed above, this blog has no apparent connection to the cited paper. It is hard to estimate the added value. The description of the cited paper is not accurate and the experiments are set up in parallel with the cited paper. This reviewer thinks that this blog is not suitable for this ICLR Blog Track, but it is a nice blog for QMIX.

[1] Mahajan, Anuj, et al. "Maven: Multi-agent variational exploration." Advances in Neural Information Processing Systems 32 (2019).
review_title: Review

Decision: Reject