Reviews: Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

The main algorithmic idea is a weighted combination of H step temporal differences, estimated on H steps (and rolled out by a learned model of the environment). The underlying idea is to allow the learner to tradeoff between estimation errors in model and Q function in different parts of the state-action space during learning. The updated TD estimator is incorporated into the DDPG algorithm in a straightforward manner. The update is computationally more intensive but the result is improved sample complexity. The experimental results on a variety of continuous control tasks show significant improvement over the baseline DDPG and a related method (MVE) (which is the precursor to this work). Overall, the paper is well written. The empirical results are very promising. The analysis and discussion is a bit limited but is not a major drawback. Overall, there is much to like about the paper.