Thank you for your rebuttal. I've read it, the global pdf, and the other reviews and associated rebuttal discussions.
W2.
I'm not a fan of only providing t-SNE because of utter lack of parameter invariance in qualitative insights. If using sklearn, the documentation [1] shows that the function has the following parameters,
class sklearn.manifold.TSNE(n_components=2, *, perplexity=30.0, early_exaggeration=12.0, learning_rate='auto', n_iter=1000, n_iter_without_progress=300, min_grad_norm=1e-07, metric='euclidean', metric_params=None, init='pca', verbose=0, random_state=None, method='barnes_hut', angle=0.5, n_jobs=None)
and parameters, such as perplexity, that greatly affect the visualization are not varied or expressly detailed.
Regardless, the clusters are distinct though the paper would be stronger if a reasonable attempt at reproducibility of the t-SNE plots were made possible. It may not be possible to make it completely reproducible, as t-SNE has a non-convex loss function. However, if the only difference is randomization, then with such nice clusters, there should be no real issue getting nice clusters again unless there was an issue acquired nice clusters originally. I'd suggest providing as much information as possible on your process for creating t-SNE plots, as a general practice since it is little effort, and pairing t-SNE with a data visualization method that is less user-manipulatable.
W4.
Regarding works using contrastive learning in MTRL. I apologize, I was over-reaching by phrasing it this way. Instead this work is MTRL using contrastively learned MoE embeddings + context task embeddings. So for learning distinct experts in MoE setting, there are continual learning methods that perform OoD detection prior to creating a new task [3, 4] and/or use similarity measures such as a Kullback-Leibler, Jensen-Shannon loss or Wasserstein distance loss to encourage dissimilarity between experts.
There are also approaches that use contrastive learning on MoEs to learn distinct experts [5].
I don't grasp the point(s) being made in the remainder of this paragraph that is speaking to attention mechanisms.
W5.
Meta-World generalization provides a good estimate for generalization on unseen Meta-World and similar tasks, which are a tiny space of continuous control tasks that are similar to the seen Meta-World tasks.
For other items, I don't have any further comments. Thank you for your work and rebuttal. I will maintain my score as is and encourage the Authors' to consider the Reviewers' feedback and rebuttal discussions in improving their paper.
[1] https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html.
[2] Eysenbach, B., Zhang, T., Levine, S., & Salakhutdinov, R. R. (2022). Contrastive learning as goal-conditioned reinforcement learning. Advances in Neural Information Processing Systems, 35, 35603-35620.
[3] Nagabandi, A., Finn, C., & Levine, S. (2018). Deep online learning via meta-learning: Continual adaptation for model-based rl. arXiv preprint arXiv:1812.07671.
[4] Xu, M., Ganesh, S., & Pasula, P. (2022). Mixture of basis for interpretable continual learning with distribution shifts. arXiv preprint arXiv:2201.01853.
[5] Mustafa, B., Riquelme, C., Puigcerver, J., Jenatton, R., & Houlsby, N. (2022). Multimodal contrastive learning with limoe: the language-image mixture of experts. Advances in Neural Information Processing Systems, 35, 9564-9576.