Paper: Efficient Inverse Multiagent Learning (ICLR-2024)

Reviewer comment (Presentation):
The presentation can be further improved, e.g., more intuitions about the assumptions and theorems.

Author response:
**Response to W2)**: We will try to add additional intuition to the main paper, beyond what already appears at the bottom of page 6 and top of page 7 (for example), respecting the space constraints. You can also already find some additional discussion in the appendix (see page 16).

Reviewer comment (Writing):
Can you further polish the paper? Some typos: for example, in the fifth line of the abstract, should it be "to solve them"?

Author response:
**Response to Q2)**: We will do our best to thoroughly proofread the paper again to correct all grammatical, semantic, and syntactical errors in the camera-ready version.

Reviewer comment (Experiments):
Although a comparison of the method has been shown with the ARIMA model on the spanish electricity market data, it would be beneficial to have a comparison with prior methods in inverse multi-agent reinforcement learning. Especially in terms of efficiency since it's one of the main points of the paper. The abstract says that the method outperforms other widely-used methods (plural), and we only get to see it being compared with one other model which is specific to time-series data.

Author response:
**Response to W2)**: To our knowledge, all existing methods of inverse multiagent reinforment learning apply only to finite state and action Markov games, while our electricity market model is a continuous state and action Markov game. As a result, comparing to these methods would require potentially non-trivial extensions. Instead, we chose to compare our method to a statistical method, namely that of ARIMA, only. We will adjust our language to correct the plural.

Reviewer comment (Novelty):
Some comparison/contextualization with prior work in multiagent inverse reinforcement learning would also be helpful.

Author response:
**Response to W3)**: A more extensive related works section is included in the third section of the appendix. There, we summarize related work not only in inverse multiagent reinforcement learning, but also in microeconomics, econometrics, and algorithmic game theory. Thank you for pointing out that a reference to this more detailed related work section is missing. We will be sure to correct this oversight in the camera-ready version.