Towards Simplicity in Deep Reinforcement Learning: Streamlined Off-Policy Learning
The field of Deep Reinforcement Learning (DRL) has recently seen a surge in the popularity of maximum entropy reinforcement learning algorithms. Their popularity stems from the intuitive interpretation of the maximum entropy objective and their superior sample efficiency on standard benchmarks. In this paper, we seek to understand the primary contribution of the entropy term to the performance of maximum entropy algorithms. For the Mujoco benchmark, we demonstrate that the entropy term in Soft Actor Critic (SAC) principally addresses the bounded nature of the action spaces. With this insight, we propose a simple normalization scheme which allows a streamlined algorithm without entropy maximization match the performance of SAC. Our experimental results demonstrate a need to revisit the benefits of entropy regularization in DRL. We also propose a simple non-uniform sampling method for selecting transitions from the replay buffer during training. We further show that the streamlined algorithm with the simple non-uniform sampling scheme outperforms SAC and achieves state-of-the-art performance on challenging continuous control tasks.
The authors investigate the role of entropy maximization in SAC and show that entropy regularization does not do what is usually thought: in the examples they investigate, where the output of the policy network needs to be squashed to fit in the action space domain, squashing would result in having only action at the boundaries, but entropy regularization maintains some intermediate values, hence exploration. From this insight, the authors replace entropy regularization by a simpler normalization process and show equivalent performance with their simpler Streamlined Off-Policy (SOP) algorithm. Then they introduce a second *Emphasizing Recent Experience* mechanism and show that SOP+ERE performs better than SAC. A good point for the paper is that the entropy regularization study is very nice, more papers in the field should show similar detailed analyses of internal processes. But the paper suffers from a few serious weaknesses: - The TD3 mechanism goes beyond the Double Q-learning (or DDQN) mechanism of Van Hasselt et al: it takes the min over two critics. This should be explained properly. - the title, abstract and introduction insist more on SOP, but performance improvement seem to result more from ERE. If this is possible, studying the performance of SAC + ERE would disambiguate the relative contribution of both mechanisms. About gradient squashing issues, the authors main mention de gradient inverter idea from this paper: @article{hausknecht2015deep, title={Deep reinforcement learning in parameterized action space}, author={Hausknecht, Matthew and Stone, Peter}, journal={arXiv preprint arXiv:1511.04143}, year={2015} } The authors should also probably also cite (and read the latest arxiv version of): @inproceedings{ahmed2019understanding, title={Understanding the impact of entropy on policy optimization}, author={Ahmed, Zafarali and Le Roux, Nicolas and Norouzi, Mohammad and Schuurmans, Dale}, booktitle={International Conference on Machine Learning}, pages={151--160}, year={2019} } More local points: - *without performing a careful hyper-parameter search*: so how did you choose these hyper-parameters? I see what you mean, but this is a very vague and slippery statement. - I do not find the 23*4 images in Appendix B much useful - Fig 3 seems to be repeated in Fig 4. Can*t you just remove Fig 3?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Towards Simplicity in Deep Reinforcement Learning: Streamlined Off-Policy Learning
The field of Deep Reinforcement Learning (DRL) has recently seen a surge in the popularity of maximum entropy reinforcement learning algorithms. Their popularity stems from the intuitive interpretation of the maximum entropy objective and their superior sample efficiency on standard benchmarks. In this paper, we seek to understand the primary contribution of the entropy term to the performance of maximum entropy algorithms. For the Mujoco benchmark, we demonstrate that the entropy term in Soft Actor Critic (SAC) principally addresses the bounded nature of the action spaces. With this insight, we propose a simple normalization scheme which allows a streamlined algorithm without entropy maximization match the performance of SAC. Our experimental results demonstrate a need to revisit the benefits of entropy regularization in DRL. We also propose a simple non-uniform sampling method for selecting transitions from the replay buffer during training. We further show that the streamlined algorithm with the simple non-uniform sampling scheme outperforms SAC and achieves state-of-the-art performance on challenging continuous control tasks.
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>



The authors investigate the role of entropy maximization in SAC and show that entropy regularization does not do what is usually thought: in the examples they investigate, where the output of the policy network needs to be squashed to fit in the action space domain, squashing would result in having only action at the boundaries, but entropy regularization maintains some intermediate values, hence exploration. From this insight, the authors replace entropy regularization by a simpler normalization process and show equivalent performance with their simpler Streamlined Off-Policy (SOP) algorithm. Then they introduce a second *Emphasizing Recent Experience* mechanism and show that SOP+ERE performs better than SAC. A good point for the paper is that the entropy regularization study is very nice, more papers in the field should show similar detailed analyses of internal processes. But the paper suffers from a few serious weaknesses: - The TD3 mechanism goes beyond the Double Q-learning (or DDQN) mechanism of Van Hasselt et al: it takes the min over two critics. This should be explained properly. - the title, abstract and introduction insist more on SOP, but performance improvement seem to result more from ERE. If this is possible, studying the performance of SAC + ERE would disambiguate the relative contribution of both mechanisms. About gradient squashing issues, the authors main mention de gradient inverter idea from this paper: @article{hausknecht2015deep, title={Deep reinforcement learning in parameterized action space}, author={Hausknecht, Matthew and Stone, Peter}, journal={arXiv preprint arXiv:1511.04143}, year={2015} } The authors should also probably also cite (and read the latest arxiv version of): @inproceedings{ahmed2019understanding, title={Understanding the impact of entropy on policy optimization}, author={Ahmed, Zafarali and Le Roux, Nicolas and Norouzi, Mohammad and Schuurmans, Dale}, booktitle={International Conference on Machine Learning}, pages={151--160}, year={2019} } More local points: - *without performing a careful hyper-parameter search*: so how did you choose these hyper-parameters? I see what you mean, but this is a very vague and slippery statement. - I do not find the 23*4 images in Appendix B much useful - Fig 3 seems to be repeated in Fig 4. Can*t you just remove Fig 3?
assistant
The authors investigate the role of entropy maximization in SAC and show that entropy regularization does not do what is usually thought: in the examples they investigate, where the output of the policy network needs to be squashed to fit in the action space domain, squashing would result in having only action at the boundaries, but entropy regularization maintains some intermediate values, hence exploration. From this insight, the authors replace entropy regularization by a simpler normalization process and show equivalent performance with their simpler Streamlined Off-Policy (SOP) algorithm. Then they introduce a second *Emphasizing Recent Experience* mechanism and show that SOP+ERE performs better than SAC. A good point for the paper is that the entropy regularization study is very nice, more papers in the field should show similar detailed analyses of internal processes. But the paper suffers from a few serious weaknesses: - The TD3 mechanism goes beyond the Double Q-learning (or DDQN) mechanism of Van Hasselt et al: it takes the min over two critics. This should be explained properly. - the title, abstract and introduction insist more on SOP, but performance improvement seem to result more from ERE. If this is possible, studying the performance of SAC + ERE would disambiguate the relative contribution of both mechanisms. About gradient squashing issues, the authors main mention de gradient inverter idea from this paper: @article{hausknecht2015deep, title={Deep reinforcement learning in parameterized action space}, author={Hausknecht, Matthew and Stone, Peter}, journal={arXiv preprint arXiv:1511.04143}, year={2015} } The authors should also probably also cite (and read the latest arxiv version of): @inproceedings{ahmed2019understanding, title={Understanding the impact of entropy on policy optimization}, author={Ahmed, Zafarali and Le Roux, Nicolas and Norouzi, Mohammad and Schuurmans, Dale}, booktitle={International Conference on Machine Learning}, pages={151--160}, year={2019} } More local points: - *without performing a careful hyper-parameter search*: so how did you choose these hyper-parameters? I see what you mean, but this is a very vague and slippery statement. - I do not find the 23*4 images in Appendix B much useful - Fig 3 seems to be repeated in Fig 4. Can*t you just remove Fig 3?