Policy Optimization with Stochastic Mirror Descent
Improving sample efficiency has been a longstanding goal in reinforcement learning. In this paper, we propose the : a sample efficient policy gradient method with stochastic mirror descent. A novel variance reduced policy gradient estimator is the key of to improve sample efficiency. Our needs only sample trajectories to achieve an -approximate first-order stationary point, which matches the best-known sample complexity. We conduct extensive experiments to show our algorithm outperforms state-of-the-art policy gradient methods in various settings.
This paper proposes a new policy gradient method that is based on stochastic mirror descent. It has been shown theoretically and empirically that the method achieves more sample efficiency. Overall, the proposed idea is interesting. The paper has good contributions in both theoretical and algorithmic aspects to policy optimization family. The empirical results are also very promising. I have only some following concerns. - It would be really nice if the discussions on connections to existing policy gradient methods on page 4 can be elaborated. This is to make the readers understand better how the reduction from the proposed method to an existing one can be made. It would also be more interesting after such reductions can be made, one can compare the sample efficiency of the proposed method with such state-of-the-art policy gradient methods. That means Table 1 can be extended with rows of such methods. - The convergence analysis in Theorem 2 and 3 are really strong, but it would be very useful if there are ablations that can reflect the trade-off between the convergence property and hyperparamters used in those theorems. - The contribution of the Bregman distance D to the update should be worth further discussions. It is an important part of SMD, but for policy optimization one might want to see how it contributes to stabilizing the update OR policy exploration. As seen, with a different choice of D, the method is reduced to policy gradient or entropy-regularized policy optimization. Ablations without D might show some interesting results too. - Some minor comments: page 8: *The result in Figure 1 shows that MPO converges faster significantly and achieves a better performance than both REINFORCE and MPO.* -> should be *... both REINFORCE and VPG.*
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Policy Optimization with Stochastic Mirror Descent
Improving sample efficiency has been a longstanding goal in reinforcement learning. In this paper, we propose the : a sample efficient policy gradient method with stochastic mirror descent. A novel variance reduced policy gradient estimator is the key of to improve sample efficiency. Our needs only sample trajectories to achieve an -approximate first-order stationary point, which matches the best-known sample complexity. We conduct extensive experiments to show our algorithm outperforms state-of-the-art policy gradient methods in various settings.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper proposes a new policy gradient method that is based on stochastic mirror descent. It has been shown theoretically and empirically that the method achieves more sample efficiency. Overall, the proposed idea is interesting. The paper has good contributions in both theoretical and algorithmic aspects to policy optimization family. The empirical results are also very promising. I have only some following concerns. - It would be really nice if the discussions on connections to existing policy gradient methods on page 4 can be elaborated. This is to make the readers understand better how the reduction from the proposed method to an existing one can be made. It would also be more interesting after such reductions can be made, one can compare the sample efficiency of the proposed method with such state-of-the-art policy gradient methods. That means Table 1 can be extended with rows of such methods. - The convergence analysis in Theorem 2 and 3 are really strong, but it would be very useful if there are ablations that can reflect the trade-off between the convergence property and hyperparamters used in those theorems. - The contribution of the Bregman distance D to the update should be worth further discussions. It is an important part of SMD, but for policy optimization one might want to see how it contributes to stabilizing the update OR policy exploration. As seen, with a different choice of D, the method is reduced to policy gradient or entropy-regularized policy optimization. Ablations without D might show some interesting results too. - Some minor comments: page 8: *The result in Figure 1 shows that MPO converges faster significantly and achieves a better performance than both REINFORCE and MPO.* -> should be *... both REINFORCE and VPG.*
assistant
This paper proposes a new policy gradient method that is based on stochastic mirror descent. It has been shown theoretically and empirically that the method achieves more sample efficiency. Overall, the proposed idea is interesting. The paper has good contributions in both theoretical and algorithmic aspects to policy optimization family. The empirical results are also very promising. I have only some following concerns. - It would be really nice if the discussions on connections to existing policy gradient methods on page 4 can be elaborated. This is to make the readers understand better how the reduction from the proposed method to an existing one can be made. It would also be more interesting after such reductions can be made, one can compare the sample efficiency of the proposed method with such state-of-the-art policy gradient methods. That means Table 1 can be extended with rows of such methods. - The convergence analysis in Theorem 2 and 3 are really strong, but it would be very useful if there are ablations that can reflect the trade-off between the convergence property and hyperparamters used in those theorems. - The contribution of the Bregman distance D to the update should be worth further discussions. It is an important part of SMD, but for policy optimization one might want to see how it contributes to stabilizing the update OR policy exploration. As seen, with a different choice of D, the method is reduced to policy gradient or entropy-regularized policy optimization. Ablations without D might show some interesting results too. - Some minor comments: page 8: *The result in Figure 1 shows that MPO converges faster significantly and achieves a better performance than both REINFORCE and MPO. * -> should be *... both REINFORCE and VPG. *