text: The authors combine an ensemble of DNNs as model for the dynamics with TRPO.  The ensemble is used in two steps: First to collect imaginary roll-outs for TRPO and secondly to estimate convergence of the algorithm.  The experiments indicate superior performance over the baselines. The paper is well-written  and the experiments indicate good results.  However, idea of using ensembles in the context of (model-based) RL  is not novel,  and it comes at the cost of time complexity.   Therefore, the method should utilize the advantage an ensemble provides to its full extent.  The main strength of an ensemble is to provide lower test error, but also some from of uncertainty estimate given by the spread of the predictions.  The authors mainly utilize the first, but to a lesser extent the second advantage (the imaginary roll-outs will  utilize the spread to generate possible outcomes).  Ideally the exploration should also be guided by the uncertainty (such as VIME). Related, what where the arguments in favor of an ensemble compared to Bayesian neural networks (possibly even as simple as using MH-dropout)?  BNNs provide a stronger theoretical justification that the predictive uncertainty is meaningful. Can the authors comment on the time-complexity of the proposed methods compared to the baselines?  In Fig. 2  the x-axis is the time step of the  real data.  But I assume it took a different amount of time for each method to reach step t.  The same argument can be made for Fig. 4.  It seems here that in snake the larger ensembles reach convergence the quickest,  but I expect this effect to be reversed when considering actual training time. In total I think this paper can provide a useful addition to the literature.   However, the proposed approach does not have strong novelty  and I am not fully convinced if the additional burden on time complexity outweighs the improved performance. Minor:  In Sec. 2: "Both of these approaches assume a fixed dataset of samples which are collected before the algorithm starts operating."   This is incorrect,  while these methods consider the domain of fixed datasets,  the algorithms themselves are not limited to this context.