text: This work introduces a particular parametrization of a stochastic policy (a uniform mixture of deterministic policies). They find this parametrization, when trained with stochastic value gradient outperforms DDPG on several OpenAI gym benchmarks. This paper unfortunately misses many significant pieces of prior work training stochastic policies. The most relevant is [1] which should definitely be cited. The algorithm here can be seen as SVG(0) with a particular parametrization of the policy. However, numerous other works have examined stochastic policies including [2] (A3C which also used the Torcs environment) and [3]. The wide use of stochastic policies in prior work makes the introductory explanation of the potential benefits for stochastic policies distracting, instead the focus should be on the particular choice and benefits of the particular stochastic parametrization chosen here and the choice of stochastic value gradient as a training method (as opposed to many on-policy methods). The empirical comparison is also hampered by only comparing with DDPG, there are numerous stochastic policy algorithms that have been compared on these environments. Additionally, the DDPG performance here is lower for several environments than the results reported in Henderson et al. 2017 (cited in the paper, table 2 here, table 3 Henderson) which should be explained. While this particular parametrization may provide some benefits, the lack of engagement with relevant prior work and other stochastic baselines significant limits the impact of this work and makes assessing its significance difficult. This work would benefit from careful copyediting. [1] Heess, N., Wayne, G., Silver, D., Lillicrap, T., Erez, T., & Tassa, Y. (2015). Learning continuous control policies by stochastic value gradients. In Advances in Neural Information Processing Systems (pp. 2944-2952). [2] Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., ... & Kavukcuoglu, K. (2016, June). Asynchronous methods for deep reinforcement learning. In International Conference on Machine Learning (pp. 1928-1937). [3] Schulman, J., Moritz, P., Levine, S., Jordan, M., & Abbeel, P. (2015). High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:1506.02438.