Official Review · ICLR 2024

This paper proposes datasets for offline multi-agent reinforcement learning including games(real world problems) with both discrete and continuous actions. The paper also provide different types of the datasets: Good, Medium, Poor. Evaluation results of offline multi-agent reinforcement learning baselines are provided. 1.The dataset for offline multi-agent reinforcement learning is missing, which is important for this community. 2.The paper provides a comprehensive dataset including both games and real world problem, both discrete and continuous. 3.The paper is well written. 1.The major concern of the paper is the correctness of the implementation of the baselines. OMAR definitely outperforms CQL in many tasks, as reported in "Beyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning https://arxiv.org/abs/2307.01472". However, this is not true in Table D.5. As a dataset and benchmark paper, I think it's crucial to ensure that the results are replicable and the claims made for previous baselines are correct. 2.The paper does not provide explanations on why an algorithm outperforms another algorithm. See the above section.