Despite significant progress in Robotic Object Search (ROS) over the recent years with deep reinforcement learning based approaches, the sparsity issue in reward setting as well as the lack of interpretability of the previous ROS approaches leave much to be desired. We present a novel policy learning approach for ROS, based on a hierarchical and interpretable modeling with intrinsic/extrinsic reward setting, to tackle these two challenges. More specifically, we train the low-level policy by deliberating between an action that achieves an immediate sub-goal and the one that is better suited for achieving the final goal. We also introduce a new evaluation metric, namely the extrinsic reward, as a harmonic measure of the object search success rate and the average steps taken. Experiments conducted with multiple settings on the House3D environment validate and show that the intelligent agent, trained with our model, can achieve a better object search performance (higher success rate with lower average steps, measured by SPL: Success weighted by inverse Path Length). In addition, we conduct studies w.r.t. the parameter that controls the weighted overall reward from intrinsic and extrinsic components. The results suggest it is critical to devise a proper trade-off strategy to perform the object search well.
This paper proposed a hierarchical approach to perform robotic object search (ROS). 
The idea is to use a high-level policy which produces subgoals and a low-level policy which produces atomic actions conditioned on both the subgoals and the true goal, and which is trained with a weighted sum of the original extrinsic reward and a reward for reaching the subgoal. Subgoals are consist of different objects in the field of view which the robot can choose to approach. 
The approach is evaluated on the House3D dataset, where it is shown to perform well. 
Recommendation: weak reject. 
This isn't a bad paper, but I'm not sure it will be of broad interest at ICLR.
It is very specific to ROS problem and House3D dataset and doesn't seem to propose a general algorithm which can be broadly applicable elsewhere. The mechanism for generating subgoals and training the low-level policy is very task dependent (subgoals are constrained to be objects in the field of view, the intrinsic reward for training the low level policy is dependent on the size of the bounding box of the object defining the subgoal). While this probably accounts for improved performance on the House3D dataset, I think the audience of ICLR would be more interested in a general approach which can be used in many different domains (even at the cost of performing less well on a specific domain than something more tailored). This paper may be a better fit for a robotics conference. 
Another point concerning the experimental evaluation. Sparsity of the rewards is mentioned as a main motivation for the hierarchical approach. However, there are a number of methods which use exploration bonuses to address this issue (pseudocounts, random network distillation, ICM etc. [1, 2, 3]). At least one of these should be included as a baseline.
Specific comments:
- using two letters for denote a single variable is confusing, since it seems like a product. I.e. using "sg" to denote a subgoal, "at_t" to denote area. Please use a single letter and add subscripts if necessary to disambiguate.
- in the various equations, please use "\log" instead of "log" so that it is not italicized.
- bottom of page 4: "Q-leaning" -> "Q-learning"
- page 2: "way pf" -> "way of"
- please use more informative names for Settings A/B
- First paragraph in Section 2: "hierarchical policy for the robot to perform the object search, motivated by how human beings conduct object search". Saying the method is similar to how humans behave is a fairly big claim that should be substantiated by appropriate references, or not made at all.  
References:
[1] https://arxiv.org/abs/1810.12894
[2] https://arxiv.org/abs/1703.01310
[3] https://arxiv.org/abs/1705.05363