From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper35/Authors
comment: Thanks for your comments!

The unified policy is generated by the decoder that takes concatenation of social contexts and vehicle dynamics from the 'History social pooling' (upper part)  and 'Future social pooling' (lower part). The lower part makes use of level-0 policy to produce the output of Future social pooling. In the paper we implement for until level-1 so the unified policy is the level-1 policy. But in general for level-k, the first k-1 policies would be used as input to get the output of the Future social pooling and then concatenated with 'History social tensor' output followed by decoder. 

Line 80-81 says that we concatenate to merge:
"The decoder LSTM layer receives as input the concatenation of the both past and future “social contexts” and the “vehicle dynamics” vectors ..."

We will add the explanation in the camera ready if the manuscript is accepted. 
title: Clarification on how policies from different levels are combined

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper35/AnonReviewer1
evaluation: The paper introduces Multi-Fidelity Recursive Behaviour Prediction (MFRBP), a method to reason about the movements of agents, when there are more than one agent in the environment.

The main idea behind the method is to generate policies at various different levels of reasoning. These levels correspond to the levels in cognitive hierarchy models. The paper has empirically demonstrated that MFRBP surpasses the state-of-the-art methods on benchmark datasets. This topic pertains to the workshop, as it provides a model to incorporate game theory into movement prediction of agents.

It would be appreciated if the paper could contain more details on how the policies generated from each level is used to obtain a unified policy. There was no elaboration on how the merging was done.

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper35/AnonReviewer1
relevance: 4: high
impact: 4: high
novelty: 3: borderline