Additional Feedback: I like authors tried their experiments in various perspectives, but experience sharing is occasionally seen from the existing literature. For example, although it wasn't mentioned in the paper, [1] used experience sharing among agents for their implementation, and I believe there may be other works with the topic of "MARL for homogeneous agents". The main reason I score "below acceptance" is that quite weak baselines seem to be used: - In Table 1, QMIX and MADDPG highly underperforms SEAC and other baselines (IAC, SNAC). However, since methods with CTDE are mostly more stable than independent learning methods, I think this part should be explained in more detail. Although other reviewers have argued the strength of this work from the importance weighting and simplicity of methods, I still think there should have been stronger baselines.

This paper proposes several alternative extensions of GAIL to multi-agent imitation learning settings. The paper includes strong, positive results on a wide range of environments against a suitable selection of baselines. However, insufficient details of the environments is provided to reproduce or fully appreciate the complexity of these environments. If accepted, I would request the authors add these to the appendix and would appreciate details (space permitting) to be discussed in the rebuttal - particularly the state representation. The more pressing point I would like to raise for discussion in the rebuttal is with regard to the MACK algorithm proposed for the generator. The authors make a justified argument for the novelty of the algorithm, but do not thoroughly justify why they used this algorithm instead of an established MARL algorithm (e.g.

Imitation learning algorithms can be used to learn a policy from expert demonstrations without access to a reward signal. However, most existing approaches are not applicable in multi-agent settings due to the existence of multiple (Nash) equilibria and non-stationary environments. We propose a new framework for multi-agent imitation learning for general Markov games, where we build upon a generalized notion of inverse reinforcement learning. We further introduce a practical multi-agent actor-critic algorithm with good empirical performance. Our method can be used to imitate complex behaviors in high-dimensional environments with multiple cooperative or competing agents. Papers published at the Neural Information Processing Systems Conference.