Paper:
We consider a setting where biases are involved when agents internalise an environment. Agents have different biases, all of which resulting in imperfect evidence collected for taking optimal actions. Throughout the interactions, each agent asynchronously internalises their own predictive model of the environment and forms a virtual simulation within which the agent plays trials of the episodes in entirety. In this research, we focus on developing a collective policy trained solely inside agents' simulations, which can then be transferred to the real-world environment. The key idea is to let agents imagine together; make them take turns to host virtual episodes within which all agents participate and interact with their own biased representations. Since agents' biases vary, the collective policy developed while sequentially visiting the internal simulations complement one another's shortcomings. In our experiment, the collective policies consistently achieve significantly higher returns than the best individually trained policies.

Review:
This paper presents a model-based RL architecture based on World Models which additionally makes use of an ensemble of models. Specifically, each model in the ensemble is independently trained models and has different amounts and types of occlusions (i.e. part of the image cropped out or a small piece of the image is magnified). The ensemble of models are used to train what is referred to as a “collective” policy in the CarRacing and VizDoom environments. The collective policy achieves better performance than policies trained using the individual models and achieves higher data efficiency than model-free RL.
While I think the paper tackles an interesting question---how to leverage multiple models of the world that may be trained on different data and with different capabilities---I unfortunately do not feel that this paper is ready for publication at ICLR, and thus recommend rejection. My justifications are that (1) the paper suffers from clarity issues and several cases makes claims or statements which are incorrect, (2) the proposed evaluation is somewhat contrived and the method does not seem to generalize well to other domains, and (3) there is not much engagement with the rest of the literature on model-based RL. I think that addressing #1 and #3 alone would substantially improve the paper. An additional change that would make the paper much more compelling would be to train the models on more ecologically valid observations, such as from multiple modalities or different camera angles (see below for discussion).
My first concern with the paper has to do with clarity. Overall, I found the paper quite difficult to read as it uses a lot of unusual terminology to explain what it does. For example, the paper uses “rule component” to refer to what would normally be a “reward function”. Similarly, although this is an ensemble method, the term “ensemble” is never used even once. The word “bias” is also used in a way which is different from how it is normally used---here it seems to refer to the fact that the agents see different things, whereas in ML bias typically has a precise meaning in terms of the bias-variance tradeoff. I also noticed several places throughout the paper where citations or related work seem to be misunderstood:
 
- 1st page, 2nd paragraph: this is not what Griffiths (2010) is about, and is not really an appropriate use of the term “inductive bias”
- 2nd page, contribution 1: the paper claims that it is not dealing with a partially observable setting, but this is exactly what the paper is doing. Specifically, each model in the ensemble receives different partially observed observations from the environment. Although in traditional RL partially observed usually means “one agent, one observation of a state”, there is nothing limiting the general formulation of partial observability to cover the case of “multiple agents, multiple observations of the same state”.
- 8th page, line 2: Lake et al (2017) is not really about “learning to think”.
- Section 4, last paragraph: this seems to be not very relevant to this paper, as the present paper is not about a multiagent system or about graph-based representations.
 
There are some additional issues with clarity of the model explanation; for example, it’s not clear how the translator component is trained (the paper states that it is trained with meta-learning, but this is vague).
 
My second concern is that the approach is somewhat contrived and does not seem to generalize well to other domains. In particular, under what circumstances would you want to train multiple models that receive inputs that are missing different pieces? I can see the appeal of learning different models in some cases (e.g. different camera angles, or different sensory modalities) but these have not been evaluated here which makes the whole thing seem a bit arbitrary. Moreover, while it seems to work ok in the CarRacing domain (though the ensemble-trained policy does not perform as well as a non-ensemble agent which is trained with full observations) it does not seem to work well in the VizDoom environment. On the basis of these two issues it is not at all clear to me how well the proposed method will work in more ecologically valid settings requiring multiple models.
 
Finally, my third concern is that the engagement with the rest of the model-based RL literature is lacking. As mentioned above, this method is an ensemble method but there is no discussion of other model-based RL ensemble methods (e.g. [1] and [2]). Similarly, the paper dismisses other work on partial observations but this work is quite relevant to the present paper which does indeed include partial observations (see above). While there is a lot of text spent discussing other work such as literature from cognitive science, this is not really that relevant to the present paper and that space would be better spent discussing related work from model-based RL.
Some additional minor comments:
It would be interesting to see a comparison between the ensemble policy trained with models that always receive the same (full) observations versus the present approach (where they receive partial observations). This might still provide some benefit as the models will still result in slightly different latent representations due to different initialization and training data, so it would be interesting to see whether this helps as well even if the observations are the same.
What does “model tr” and “policy lr” mean in Table 1?
In the various tables and figures, how are the error bars computed? Are they over episodes, or agent seeds? In general, how many seeds were used to perform evaluations? (Ideally it would be at least 3).
Page 2, section 2.1: “V and M use sequential image frame data from multiple rollouts from a random policy and apply variational inference and backpropagation algorithm for inference and learning.” → this is imprecise; they do not apply both variational inference and backpropagation; backpropagation is used to implement variational inference.
I found the results of Figure 3d quite interesting, though am wondering if you controlled for the number of evaluations of each model? E.g. if you only train the policy on one model at a time versus training them on all models at a time, do you train for longer in the first case so that the policy ultimately receives the same amount of information from all the models?
[1] Kurutach, T., Clavera, I., Duan, Y., Tamar, A., & Abbeel, P. (2018). Model-ensemble trust-region policy optimization. arXiv preprint arXiv:1802.10592.
[2] Chua, K., Calandra, R., McAllister, R., & Levine, S. (2018). Deep reinforcement learning in a handful of trials using probabilistic dynamics models. In Advances in Neural Information Processing Systems (pp. 4754-4765).