Paper:
Creating a useful representation of the world takes more than just rote memorization of individual data samples. This is because fundamentally, we use our internal representation to plan and solve problems. In this paper, we introduce Plan2Vec, an unsupervised representation learning objective inspired by graph-planning algorithms and value-based reinforcement techniques. By abstracting away low-level control with a learned local metric, we show that it is possible to learn representations that inform long-range structures, entirely passively from high-dimensional sequential datasets that assume no access to action data or an expert policy. Plan2vec learns a latent space through value iteration on the graph formed by the data points, connected via a local metric function trained contrastively from context. We show that the global metric on this learned embedding can be used to plan with O(1) complexity by linear interpolation. This exponential speedup is critical for planning with a learned representation on any problem containing non-trivial global topology. We demonstrate the effectiveness of Plan2Vec on simulated as well as two real-world image datasets, showing that Plan2Vec can effectively acquire representations that carry long-range structure to accelerate planning. Additional results and videos can be found at https://sites.google.com/view/plan2vec.

Review:
## Paper Summary
While cast slightly differently in the intro, it seems to me that this paper learns a goal-conditioned value function that is used at test time to construct visual plans by selecting an appropriate sequence of data points from the training data. Similar to prior work they learn a local distance metric without supervision using a temporal proximity objective and then construct a graph of the training data points using this metric. The main novelty that this paper introduces seems to be the idea to distill the results of planning algorithms run at training time into a global, goal-conditioned value function, which allows to reduce the required planning time at test time. The authors perform experiments on constructing visual plans for a simulated toy navigation task, a robotic rope manipulation task and the StreetLearn navigation task. The paper reports favorable results under a time-constrained test setting but does not include strong baselines that were designed for this setting. 
## Strengths
- bootstrapping a learned local distance metric to a global distance metric to reduce test-time planning cost is an interesting problem
- the paper has nice visualizations / analysis on the toy dataset
- the learning procedure for the local distance metric is clearly described
- the paper uses a large variety of different visualizations to make concepts and results clearer 
## Weaknesses
(1) missing links to related work: the author's treatment of related work does not address the connections to some relevant papers (e.g. [1-3]) or is only done in the appendix (especially for [4]). It is not clearly delineated between techniques and ideas that are introduced in other papers (see (2) below) and the novel parts of this work. This makes it hard to understand the actual contributions of this paper. 
(2) only minor contribution: the core parts of this paper build heavily on prior work: time-contrastive objectives for distance learning have been introduced in [5] and also been used in a very similar setup as here in [4], further [4, 3] also use semi-parametric, graph-like representations for planning with a learned local distance metric. The major contribution seems to be to distill the plans derived with either (a) n-step greedy rollout or (b) Dijkstra graph-search into a value-function so that planning does not need to be performed at test time. This somewhat small contribution is in contrast to the claims from the introduction that this paper "pose[s] the problem of unsupervised learning a plannable representation as learning a cognitive map of the domain".
(3) comparison to weak baselines: the main comparison in the experimental section is to a version of [4] where the authors constrain the planning horizon to a single step, which means effectively greedily using the local metric from Sec. 3.1. To be clear: this is in no way the method of [4]: they use Dijkstra-based planning at test time and it is clear that a "version" of [4] that does not use planning is not able to work. To me this seems rather like an ablation of the proposed method than a real baseline. The baseline that plans greedily with embeddings based on visual similarity has even less hope of working. The paper lacks thorough comparison to (a) baselines with the same semi-parametric structure that perform planning at test time (like the real method of [4]) and (b) methods that generate reactive policies without constructing a semi-parametric memory (e.g. off-policy RL). Only then a thorough comparison of pros and cons of planning at training/test time is possible (see detailed suggestions below).
(4) lack of qualitative samples for generated plans: for both the rope and the StreetLearn domain the authors do not provide thorough evaluation. For the rope domain only a single qualitative rollout is shown, for the StreetLearn domain no qualitative samples are provided for either the proposed method or the comparisons. (see suggestions for further evaluation below)
(5) explanation of core algorithmic part unclear: the explanation of how the local metric is used to learn the global value function is somewhat unclear and the used notation is confusing. Key problems seem to be the double-introduction of symbols for the local metric in Alg. 2 and the confusing usage of the terms "global embedding" and "value function" (see detailed questions below) 
(6) terms used / writing structure makes paper hard to follow: the connection between used concepts like "global embedding", "plannable representation" and "goal-conditioned value function" are not clear in the writing of the paper. The authors talk about concepts without introducing them clearly before (e.g. problems of RL are listed in the intro without any prior reference to RL).
(7) lacks detail for reproducing results: the paper does not provide sufficient detail for reproducing the results. Neither in the main paper nor in the appendix do the authors provide details regarding architecture and used hyperparameters. It is unclear what policy was used to collect the training data, it is unclear how the baselines are working in detail (e.g. how the 1-step planning works) and how produced plans are checked for their validity.
## Questions
(A) What policy is used to collect the training data on each environment?
(B) What is the relation between the "global embedding" \Phi and the "goal-conditioned value function" V_\Phi(x, x_prime) in Algorithm 2? 
(C) What is the difference between the local metric function \phi and the reward function in Algorithm 2? Are they the same?
(D) If they are the same, how can the local metric accurately estimate rewards for states x and x_g that are far apart from one another as would naturally be the case when training the value function?
(E) What does the notation N(1, \eps) in line 5 of Algorithm 2 mean?
(F) What is the expectation over the length of trajectories between start and goal on the StreetLearn environment (to estimate what percentage of that the success horizon of 50 steps is)?
## Suggestions to improve the paper
(for 1) please add a more thorough treatment of the closest related works on semi-parametric memory + learned visual planning + learned distance functions (some mentioned below [1-5]) to the main part of the paper, clearly stating differences and carving out which parts are similar and where actual novelty lies.
(for 2) please explain clearly the added value of distilling the training-plans into a value function for O(1) test-time planning and point out that this is the main difference e.g. to [4] and therefore the main contribution of the paper. 
(for 3) in order to better understand the trade-offs between doing planning at test time (like [3,4]) or learning an O(1) planner contrast runtime and performance of both options (i.e. compare to the proper method of [4]). This will help readers understand how much speed they gain from the proposed method vs how much performance they loose. It might also make sense to include an off-policy RL algorithm (e.g. SAC) that uses the local metric as reward function (without constructing the graph) to investigate how much planning via graph-search can help at training time. Another interesting direction can be to investigate the generalization performance to a new environment (e.g. new street maze, new rope setup) after training on a variety of environment configurations. [3] showed that explicit test-time planning performs better than "pure" RL, it would be interesting how the proposed "hybrid" approach performs.
(for 4) please add randomly sampled qualitative results for both environments and all methods to the appendix. It can additionally be helpful to add GIFs of executions to a website. It might also be interesting to add a quantitative evaluation for the plans from the rope environment as was performed in Kurutach et al. 2018.
(for 5) please incorporate answers to questions (B-E) into the text in Sec 3.2 explaining Algorithm 2. It might also help to structure the text in such a way as to follow the flow of the algorithm.
(for 6) restructure and shorten the introduction, clarify terms like "inductive prior within image generation" or "non-local concepts of distances and direction" or "conceptual reward" or "planning network", clarify how the authors connect the proposed representation learning objective and RL. Avoid sentences that are a highly compressed summary of the paper but for which the reader lacks background, like in the intro: "training a planning agent to master an imagined “reaching game” on a graph".
(for 7) add details for architecture and hyperparameters to the appendix, add details for how baselines are constructed to the appendix. add details about data collection and evaluation for all datasets to the appendix (e.g. how is checked that a plan is coherent in StreetLearn). It might also help to add an algorithm box for the test time procedure for the proposed method.
## Minor Edit Suggestions
- Fig 2 seems to define the blue square as the target, the text next to it describes the blue square as the agent, please make coherent
- for Fig 7: the numbers contained in the figure are not explained in the caption, especially the numbers below the images are cryptic, please explain or omit
[Novelty]: minor
[technical novelty]: minor
[Experimental Design]: Okay
[potential impact]: minor
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[overall recommendation]: weakReject - The exposition of the problem and treatment of related work are not sufficient, the actual novelty of the proposed paper is low and the lack of comparison to strong baselines push this paper below the bar for acceptance.
[Confidence]: High
[1] Cognitive Planning and Mapping, Gupta et al., 2017
[2] Universal Planning Networks, Srinivas et al., 2018
[3] Search on the Replay Buffer: Bridging Planning and Reinforcement Learning, Eysenbach et al., 2019
[4] Semi-Parametric Topological Memory for Navigation, Savinov et al., 2018
[5] Time-Contrastive Networks, Sermanet et al., 2017
### Post-rebuttal reply ###
I appreciate the author's reply, the experiments that were added during the rebuttal are definitely a good step forward. The authors added comparison to a model-free RL baseline as well as proper comparison to a multi-step planning version of SPTM. However, these comparisons were only performed on the most simple environment: the open room environment without any obstacle. These evaluations are not sufficient to prove the merit of the proposed method, especially given that it is sold as an alternative to planning methods. The method needs to be tested against fair baselines on more complicated environments; the current submission only contains baselines that *cannot* work on the more complicated tasks. I therefore don't see grounds to improve my rating.