Paper:
DIVIDE-AND-CONQUER REINFORCEMENT LEARNING
Standard model-free deep reinforcement learning (RL) algorithms sample a new initial state for each trial, allowing them to optimize policies that can perform well even in highly stochastic environments. However, problems that exhibit considerable initial state variation typically produce high-variance gradient estimates for model-free RL, making direct policy or value function optimization challenging. In this paper, we develop a novel algorithm that instead partitions the initial state space into "slices", and optimizes an ensemble of policies, each on a different slice. The ensemble is gradually unified into a single policy that can succeed on the whole state space. This approach, which we term divide-and-conquer RL, is able to solve complex tasks where conventional deep RL methods are ineffective. Our results show that divide-and-conquer RL greatly outperforms conventional policy gradient methods on challenging grasping, manipulation, and locomotion tasks, and exceeds the performance of a variety of prior methods. Videos of policies learned by our algorithm can be viewed at https://sites.google.com/view/dnc-rl/.

Review:
This paper presents a method for learning a global policy over multiple different MDPs (referred to as different "contexts", each MDP having the same dynamics and reward, but different initial state).  The basic idea is to learn a separate policy for each context, but regularized in a manner that keeps all of them relatively close to each other, and then learn a single centralized policy that merges the multiple policies via supervised learning.  The method is evaluated on several continuous state and action control tasks, and shows improvement over existing and similar approaches, notably the Distral algorithm.
I believe there are some interesting ideas presented in this paper, but in its current form I think that the delta over past work (particularly Distral) is ultimately too small to warrant publication at ICLR.  The authors should correct me if I'm wrong, but it seems as though the algorithm presented here is virtually identical to Distral except that:
1) The KL divergence term regularizes all policies together in a pairwise manner.
2) The distillation step happens episodically every R steps rather than in a pure SGD manner.
3) The authors possibly use a TRPO type objective for the standard policy gradient term, rather than REINFORCE-like approach as in Distral (this one point wasn't completely clear, as the authors mention that a "centralized DnC" is equivalent to Distral, so they may already be adapting it to the TRPO objective? some clarity on this point would be helpful).
Thus, despite better performance of the method over Distral, this doesn't necessarily seem like a substantially new algorithmic development.  And given how sensitive RL tasks are to hyperparameter selection, there needs to be some very substantial treatment of how the regularization parameters are chosen here (both for DnC and for the Distral and centralized DnC variants).  Otherwise, it honestly seems that the differences between the competing methods could be artifacts of the choice of regularization (the alpha parameter will affect just how tightly coupled the control policies actually are).
In addition to this point, the formulation of the problem setting in many cases was also somewhat unclear.  In particular, the notion of the contextual MDP is not very clear from the presentation.  The authors define a contextual MDP setting where in addition to the initial state there is an observed context to the MDP that can affect the initial state distribution (but not the transitions or reward).  It's entirely unclear to me why this additional formulation is needed, and ultimately just seems to confuse the nature of the tasks here which is much more clearly presented just as transfer learning between identical MDPs with different state distributions; and the terminology also conflicts with the (much more complex) setting of contextual decision processes (see: https://arxiv.org/abs/1610.09512).  It doesn't seem, for instance, that the final policy is context dependent (rather, it has to "infer" the context from whatever the initial state is, so effectively doesn't take the context into account at all).  Part of the reasoning seems to be to make the work seem more distinct from Distral than it really is, but I don't see why "transfer learning" and the presented contextual MDP are really all that different.
Finally, the experimental results need to be described in substantially more detail.  The choice of regularization parameters, the precise nature of the context in each setting, and the precise design of the experiments is all extremely opaque in the current presentation.  Since the methodology here is so similar to previous approaches, much more emphasis is required to better understand the (improved) empirical results in this eating.
In summary, while I do think the core ideas of this paper are interesting: whether it's better to regularize policies to a single central policy as in Distral or whether it's better to use joint regularization, whether we need two different timescales for distillation versus policy training, and what policy optimization method works best, as it is right now the algorithmic choices in the paper seem rather ad-hoc compared to Distral, and need substantially more empirical evidence.
Minor comments:
• There are several missing words/grammatical errors throughout the manuscript, e.g. on page 2 "gradient information can better estimated".