Paper:
In environments with continuous state and action spaces, state-of-the-art actor-critic reinforcement learning algorithms can solve very complex problems, yet can also fail in environments that seem trivial, but the reason for such failures is still poorly understood. In this paper, we contribute a formal explanation of these failures in the particular case of sparse reward and deterministic environments. First, using a very elementary control problem, we illustrate that the learning process can get stuck into a fixed point corresponding to a poor solution. Then, generalizing from the studied example, we provide a detailed analysis of the underlying mechanisms which results in a new understanding of one of the convergence regimes of these algorithms. The resulting perspective casts a new light on already existing solutions to the issues we have highlighted, and suggests other potential approaches.

Review:
The paper investigates why DDPG can sometimes fail in environments with sparse rewards. It presents a simple environment that helps the reader build intuition and supports the paper's empirical investigation. First, the paper shows that DDPG fails on the simple environment in ~6% of cases, despite the solution being trivial—go left from the start state. The paper then augments DDPG with epsilon-greedy-style exploration to see if the cause of these failures is simply inadequate exploration. Surprisingly, in 1% of cases DDPG still fails. The paper shows that even in these failure cases there were still rewarded transitions that could have been learned from, and investigates relationships between properties of individual runs and the likelihood of failure. The paper then explains how these failures occur: the policy drifts to always going right, and the critic converges to a piecewise constant function whose gradient goes to zero and prevents further updates to the policy. The paper then generalizes this deadlock mechanism to other continuous-action actor-critic algorithms like TD3 and discusses how function approximation helps mitigate this issue. Finally, the paper gives a brief overview of some existing potential solutions.
Currently, I recommend rejecting this paper; while it is very well-written and rigorously investigates a problem with a popular algorithm, the paper does not actually present a novel solution method. It does a great job of clearly defining and investigating a problem and shows how others have attempted to solve it in the past, but stops there. It doesn't propose a new method and/or compare the existing methods empirically, nor does it recommend a specific solution method.
A much smaller concern is that the problem investigated is somewhat niche; it happens in a very small percentage of runs and is mitigated by the type of function approximation commonly used. This lowers its potential impact a little.
The introduction does a great job of motivating the paper. I would've liked the related work section to elaborate more on the relationships between the insights in this paper and those of Fujimoto et al. (2018a), since the paper says the insights are related. Section 3 gave a very clear overview of DDPG. The simple environment described in section 4 was intuitive and explained well, and the empirical studies were convincing.
However, after section 4 established the existence of the deadlock problem in DDPG, I was expecting the rest of the paper to present a solution method and empirically validate it. Instead, section 5 generalizes the findings from section 4 to include other environments and algorithms. I felt that section 4 and 5 could have been combined and shortened, and the extra space used to explain and empirically test a novel solution method. For example, figures 6 and 7 seem to be conveying similar information, but figure 7 takes up almost half a page.
Currently this paper seems like a good candidate for a workshop. It convincingly establishes the existence of a problem and shows what causes the problem to occur, but doesn't contribute a solution to the problem. Submitting it to a workshop could be a good opportunity for discussion, feedback, and possible collaboration, all of which might help inspire a solution method.