Paper:
PROBABILISTIC PLANNING WITH SEQUENTIAL MONTE CARLO METHODS
In this work, we propose a novel formulation of planning which views it as a probabilistic inference problem over future optimal trajectories. This enables us to use sampling methods, and thus, tackle planning in continuous domains using a fixed computational budget. We design a new algorithm, Sequential Monte Carlo Planning, by leveraging classical methods in Sequential Monte Carlo and Bayesian smoothing in the context of control as inference. Furthermore, we show that Sequential Monte Carlo Planning can capture multimodal policies and can quickly learn continuous control tasks.

Review:
The authors formulate planning as sampling from an intractable distribution motivated by control-as-inference, propose to approximately sample from the distribution using a learned model of the environment and SMC, then evaluate their approach on 3 Mujoco tasks. They claim that their method compares favorably to model-free SAC and to CEM and random shooting (RS) planning with model-based RL.
This is an interesting idea and an important problem, but there appear to be several inconsistencies in the proposed algorithm and the experimental results do not provide compelling support for the algorithm. In particular,
Levine 2018 explains that with stochastic transitions, computing the posterior leads to overly optimistic behavior because the transition dynamics are not enforced, whereas the variational bound explicitly enforces that. Is that an issue here?
The value function estimated in SAC is V^\pi the value function of the current policy. The value function needed in Sec 3.2 is a different value function. Can the authors clarify on this discrepancy?
The SMC procedure in Alg 1 appears to be incorrect. It multiplies the weights by exp(V_{t+1}) before resampling. This needs to be accounted for by setting the weights to exp(-V_{t+1}) instead of uniform. See for example auxiliary particle filters.
The experimental section could be significantly improved by addressing the following points: 
* How was the planning horizon h chosen? Is the method sensitive to this choice? What is the model accuracy?
* Does CEM use a value function? If not, it seems like a reasonable baseline to consider CEM w/ a value function to summarize the values beyond the planning horizon. This will evaluate whether SMC or including the value function is important. 
* Comparing to state-of-the-art model-based RL (e.g., one of Chua et al. 2018, Kurutach et al. 2018, Buckman et al. 2018). 
* How were the task # of steps chosen? They seem arbitrary. What is the performance at 1million and 5million steps?
* Was SAC retuned for this small number of samples/steps?
* Clarify where the error bars come from in Fig 5.2 in the caption.
At the moment, SMCP is within the error bars of a baseline method.
Comments:
In the abstract, the authors claim that the major challenges in planning are: 1) model compounding errors in roll-outs and 2) the exponential search space. Their method only attempts to address 2), is that correct? If so, can the authors state that explicitly.
Recent papers (Chua et al. 2018, Kurutach et al. 2018, Buckman et al. 2018, Ha and Schmidhuber 2018) all show promising model-based results on continuous state/action tasks. These should be mentioned in the intro.
The connection between Gu et al.'s work on SMC and SAC was unclear in the intro, can the authors clarify?
For consistency, ensure that sums go to T instead of \infty.
I found the discussion of SAC at the end of Sec 2.1 confusing. As I understand SAC, it does try to approximate the gradient of the variational bound directly. Can the authors clarify what they mean?
At the end of Sec 2.2, the authors claim that the tackle the particle degeneracy issue (a potentially serious issue) by "selecting the temperature of the resampling distribution to not be too low." I could not find further discussion of this anywhere in the paper or appendix.
Sec 3.2, mentions an action prior for the first time. Where does this come from?
Sec 3.3 derives updates assuming a perfect model, but we learn a model. What are the implications of this?
Please ensure the line #'s and the algorithm line #'s match.
Model learning is not described in the main text though it is a key component of the algorithm. The appendix lacks details (e.g., what is the distribution used to model the next state?) and contradicts itself (e.g., one place says 3 layers and another says 2 layers).
In Sec 4.1, a major difference between MCTS and SMC is that MCTS runs serially, whereas SMC runs in parallel. This should be noted and then it's unclear whether SMC-Planning should really be thought of as the maximum entropy tree search equivalent of MCTS.
In Sec 4.1, the authors claim that Alpha-Go and SMCP learn proposals in similar ways. However, SMCP minimizes the KL in the reverse direction (from stated in the text). This is an important distinction.
In Sec 4.3, the authors note that Gu et al. learn the proposal with the reverse KL from SMCP. VSMC (Le et al. 2018, Naesseth et al. 2017, Maddison et al. 2017) is the analogous work to Gu et al. that learn the proposal using the same KL direction as SMCP. The authors should consider citing this work as it directly relates to their algorithm.
In Sec 4.3, the authors claim that their direction of minimizing KL is more appropriate for exploration. Gu et al. suggest the opposite in their work. Can the author's justify their claim?
In Sec 5.1, the authors provide an example of SMCP learning a multimodal policy. This is interesting, but can the authors explain when this will be helpful?
====
11/26
At this time, the authors have not responded to reviews. I have read the other reviews. Given the outstanding issues, I do not recommend acceptance.
12/7
After reading the author's response, I have increased my score. However, baselines that establish the claim that SMC improves planning which leads to improved control are missing (such as CEM + value function). Also, targeting the posterior introduces an optimism bias that is not dealt with or discussed.