Paper:
Reinforcement Learning (RL) has demonstrated promising results across several sequential decision-making tasks. However, reinforcement learning struggles to learn efficiently, thus limiting its pervasive application to several challenging problems. A typical RL agent learns solely from its own trial-and-error experiences, requiring many experiences to learn a successful policy. To alleviate this problem, we propose collaborative inter-agent knowledge distillation (CIKD). CIKD is a learning framework that uses an ensemble of RL agents to execute different policies in the environment while sharing knowledge amongst agents in the ensemble. Our experiments demonstrate that CIKD improves upon state-of-the-art RL methods in sample efficiency and performance on several challenging MuJoCo benchmark tasks. Additionally, we present an in-depth investigation on how CIKD leads to performance improvements.

Review:
This paper introduces a method for using an ensemble of deep reinforcement learning policies, where members of the ensemble are periodically updated to imitate the most promising member of the ensemble. Thus learning proceeds by performing off policy reinforcement learning updates for each individual policy, as well as some supervised learning for inter-policy imitation learning.
I start by what I view as the positive aspects about the paper:
1- The algorithm is quite simple (to understand and to implement).
2- Experimental results are performed on a variety of domains, and more importantly, each experiment is motivated by a question.
That said, I have some concerns about this paper which I list below:
1- Perhaps my biggest concern is that the approach is not motivated from a theory stand point. There has been interesting results in Osband's work [Osband, 2016] (and references therein) for randomized value functions which can serve as a foundation for this work. That said, a) Osband's results, at least immediately, are related to value-function based methods, as opposed to policy gradient b) the KL update which one could argue is the main and only significant contribution of the paper, is not justified by Osband or any other prior work c) there is not anything that this paper adds to the literature to better justify diversity through randomization and/or imitation learning based on the best member of the ensemble.
2- I have found various claims in the paper which are unclear, scientifically not true, or sometimes even contradicting. In Introduction, for example, the authors mention that the agent sometimes gets into a sub-optimal policy and may require a large number of interactions before escaping the sub optimal policy. How does gathering more data help to improve the policy? Either we are in a local maximum, which if we are doing gradient ascent, there is really not much we could do, or that we are in a saddle point, which we can escape by adding some noise to the gradient. [Jin,2017]
3- In section 4.3 the authors talk about on-policy methods requiring importance sampling (IS) ratios. To the best of my knowledge, IS is only used for off-policy learning. Can the authors provide a link to an on-policy method that does IS?
4- Again in section 4.3 authors claim and I quote "Using off-policy methods, all the policies in the ensemble can easily be updated, since off-policy update methods can perform updates from any \tau". But later on in Section 5.3 authors claim that "off-policy actor-critic methods (e.g. SAC) cannot fully utilize the other agent's or past experience." So which statement is true?
5- Again, the KL update is interesting, but is it even surprising that the KL update is necessary for an ensemble of policies updates using policy gradients? In the absence of this KL update, which the authors characterize as the method that Osband proposed, the policies could generally be arbitrarily far from one another. This means that each policy needs to perform policy evaluation using trajectories that are coming from other policies who in principle can be radically different than the policy we want to update. This means that updates will be quite "off-policy" which we know can really degrade the quality of the estimated gradient. This is perhaps why even choosing a random policy to update towards is providing "some" improvement. I think this is the real insight, but it is not really discussed at all in the paper.
6- On the same note, I do not think that one can say Osband's method is the same as CIKD but only without the KL update. Most notably, Osband's work was presented for value-function-based methods like DQN. These methods work fundamentally different than policy gradient methods, which rely on (near) on-policy updates to perform good policy improvements. In that sense, the presented results make sense, but I disagree with the framing of the results and how they are presented here.
7- In section 5.3, when the authors utilize more policy updates to have a fair comparison, are they retuning hyper parameters? Surely they need to do that, at least for hyper-parameters that are known to be super important such as the step size.
8- Overall I liked section 5.5 that is trying to dissect causes for improvement. However, it seems like that the "dominant agent" hypothesis has been rejected hastily, unless I misunderstood the experiment. The authors show that the notion of best is spread across different agents. But of course this will be the case in light of the KL update, since the policies are getting closer to one another. Can you redo the experiment in the absence of the KL update?
9- Have the authors thought about any connection between this and genetic algorithms? In genetic algorithms, the idea is the next set of candidates are chosen based on the most promising candidates in the current iteration. CIKD seems like a soft implementation of this idea.
In light of the comments above, I am voting for weak rejection, though as I said before, I do see some interesting things in this paper. I encourage the authors to think about CIKD from a theoretical lens in the future.