Paper:
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes
We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using lowdimensional latent embeddings. Our new framework correctly models the joint uncertainty in the latent parameters and the state space. We also replace the original Gaussian Process-based model with a Bayesian Neural Network, enabling more scalable inference. Thus, we expand the scope of the HiP-MDP to applications with higher dimensions and more complex dynamics.

Review:
SUMMARY
The authors consider the problem of learning multiple related tasks. It is assumed each tasks can be described by a low-dimensional vector. A multi-task neural network model can than be learned by treating this low-dimensional vector as additional input (in addition to state and action). During optimization, both these low-dimensional inputs and the network weights are optimized. The network weights are shared over all tasks. This procedure allows a new task to be modeled very quickly. Subsequently, 'virtual samples' taken from the model can be used to train a policy (using DDQN). The method is evaluated on three different task families. 
REACTION TO AUTHOR'S REPLY
Thanks for the response. It cleared up many questions I had. I changed my rating accordingly. I think it would be good to show the standard deviation over independently trained models to show the variance induced (or absence of such variance) by the (random) choice of initial tasks and possibly the model training procedure. In the toy task, it seems there is always one 'red' and one 'blue' tasks in the initial data. Does that mean the initial tasks are hand-picked (rather than randomly drawn?). 
TECHNICAL QUALITY
The used approach seems sensible for the task at hand. The development of the method section seems mostly unproblematic. Two issues / suggestions are 1) when tuning the model, shouldn't the update for the network weight take samples from the global buffer into account to avoid catastrophic forgetting for the next task? 2) I'm not sure why you need prioritization for the model update, if you are looking to minimize the mean squared error wouldn't you want to see all samples equally often?
Support for the method is given by experimental evaluation. The method is mostly compared to ablated versions, and not to alternatives from the literature (although one of the methods is supposed to be analogous to the method in [11], and there might not be many other appropriate methods to compare to - would a comparison to [2] be possible?). In any case, the selection of methods to compare to includes different aspects, such as model-free vs model based, and independent models vs. multitask vs. single model for all tasks. It is not clear whether multiple independent runs were performed for each methods. Please specify this, and specify what the error bars represent (standard deviation or standard error, one or two deviations, spread between roll-outs or between independent trials, how many trials).
Dependent on the experiment, the proposed method seems at least as good as most baselines, and (at least numerically) better than all baselines on the most challenging task. The additional material in the appendix also shows the runtime to be much faster compared to the GP-based method in [11].
NOVELTY
The general framework is relatively close to [11]. However, the use of neural networks and the possibility to model non-linear dependence and covariance between states and hidden parameters seems like an important and interesting contribution. 
SIGNIFICANCE AND RELEVANCE
Multi-task and transfer learning seem important components for RL approaches in application domains where rapid adaptation or sample efficiency are important. The proposed method is limited to domains where the difference between tasks can be captured in a low dimensional vector.
CLARITY
There are a couple of issues with the paper's clarity. If found Figure (1) hard to understand, given its early position in the text and the absense of axis labels. I also found Algorithm 1 hard to understand. The 'real' roll-out only seemed to be used in the first episode, what is done with the data after the first episode (apart from storing in global memory for next task?). Maybe split move entire if (i=0) out of the for loop (and remove the condition), than the forloop can just contain (15) if I'm not mistaken. 
The description of the experiments is missing too many details. I couldn't find in either appendix or paper how many source instances the method was trained on before evaulation on the target task for the second or third experiment. I also couldn't find how many times you independently trained the model or what error bars represent. Two source tasks for the toy tasks seems very little given that you have differences between instances across multiple axis - with just two examples would it only learn 'red' vs 'blue' since this is the most important difference?
Furthermore there are quite a few spelling errors and similar issues in the text. I'll mention some of them below under minor comments.

MINOR COMMENTS
- Typesetting: subscripts GP and BNN are typeset as products of single-letter variables
- You might want to explain the subscripts k,b,a,d in (1) briefly.
- 79: model -> to model
- since your model's epsilon distribution is not dependent on the current state, I don't think this could model hereskedasticyity (line 31). If so, please explain. (Uncertainty in weights lead to different predictive uncertainty in parts of the state-space, like a regurlar GP. But, like a regular GP this doesn't necessarily mean you model different 'noise distribution' or risk in different part of your state space )
- 'bounded number of parameters' seems somewhat ambiguous (do you mean a finite-dimensional vector, or finitely many values for this parameter?). 
- 81: GP's don't really care about high-d input representation (linear scaling in input dimensionality), tractability issues mainly concern number of samples (cubic scaling for naive implementations).
- 92-93: are delta_x and delta_y really the same weather a=E or a=W as it appears from this equation?
- 100: a linear of function -> a linear function
- 114: Sec ??
- 123: is develop -> is to develop
- 169 from which learn a policy -> please rephrase
- 174 on trained two previous -> please rephrase
- 188 "of average model"-> of the average model
- 198 Appendix ??
- 230 - I'm not sure if embedded is the right word here
- 247-249 - it would be interesting to see a comparison
- 255 - "our work"
- references: It would be good to go over all of these ones as there are many small issues. E.g. the IEEE conferences mention the year and 'IEEE' twice. Names like 'Bayesian' or 'Markov' and abbreviations like STI, HIV, POMDP, CSPBVI should be capitalized. Q-learning too. For [21] I would cite the published (ICLR) version. Publication venue is missing for [35].
- appendices: just wanted to make sure you really meant exp(-10), not 1E-10. Does the prior need to be so small? Why? What do you mean when you say the prior variance changes over training? I think with |w| you mean the number of elements, but you used notation of a norm (w is a vector, not a set).