Advantage Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines. Our goal is an algorithm that utilizes only simple and convergent maximum likelihood loss functions, while also being able to leverage off-policy data. Our proposed approach, which we refer to as advantage-weighted regression (AWR), consists of two standard supervised learning steps: one to regress onto target values for a value function, and another to regress onto weighted target actions for the policy. The method is simple and general, can accommodate continuous and discrete actions, and can be implemented in just a few lines of code on top of standard supervised learning methods. We provide a theoretical motivation for AWR and analyze its properties when incorporating off-policy data from experience replay. We evaluate AWR on a suite of standard OpenAI Gym benchmark tasks, and show that it achieves competitive performance compared to a number of well-established state-of-the-art RL algorithms. AWR is also able to acquire more effective policies than most off-policy algorithms when learning from purely static datasets with no additional environmental interactions. Furthermore, we demonstrate our algorithm on challenging continuous control tasks with highly complex simulated characters.
# Summary The paper shows that good old reward weighted regression (RWR) with value-function baseline is still state-of-the-art algorithm. # Decision The paper is well-written and provides many evaluations. The contribution should be articulated more carefully, though, taking into account that most algorithmic ideas are present in prior work (https://openreview.net/forum?id=H1gdF34FvS&noteId=Bkxi11nsdr). Perhaps, the experience replay part is somewhat novel. It seems emphasizing more that the aim is to show that simple methods are competitive rather than focusing on novelty could be a good idea. Provided that the authors incorporate the feedback of the other reviewers and update the paper accordingly, it will make a good contribution.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Advantage Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines. Our goal is an algorithm that utilizes only simple and convergent maximum likelihood loss functions, while also being able to leverage off-policy data. Our proposed approach, which we refer to as advantage-weighted regression (AWR), consists of two standard supervised learning steps: one to regress onto target values for a value function, and another to regress onto weighted target actions for the policy. The method is simple and general, can accommodate continuous and discrete actions, and can be implemented in just a few lines of code on top of standard supervised learning methods. We provide a theoretical motivation for AWR and analyze its properties when incorporating off-policy data from experience replay. We evaluate AWR on a suite of standard OpenAI Gym benchmark tasks, and show that it achieves competitive performance compared to a number of well-established state-of-the-art RL algorithms. AWR is also able to acquire more effective policies than most off-policy algorithms when learning from purely static datasets with no additional environmental interactions. Furthermore, we demonstrate our algorithm on challenging continuous control tasks with highly complex simulated characters.
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>



# Summary The paper shows that good old reward weighted regression (RWR) with value-function baseline is still state-of-the-art algorithm. # Decision The paper is well-written and provides many evaluations. The contribution should be articulated more carefully, though, taking into account that most algorithmic ideas are present in prior work (https://openreview.net/forum?id=H1gdF34FvS&noteId=Bkxi11nsdr). Perhaps, the experience replay part is somewhat novel. It seems emphasizing more that the aim is to show that simple methods are competitive rather than focusing on novelty could be a good idea. Provided that the authors incorporate the feedback of the other reviewers and update the paper accordingly, it will make a good contribution.
assistant
# Summary The paper shows that good old reward weighted regression (RWR) with value-function baseline is still state-of-the-art algorithm. # Decision The paper is well-written and provides many evaluations. The contribution should be articulated more carefully, though, taking into account that most algorithmic ideas are present in prior work (https://openreview.net/forum?id=H1gdF34FvS&noteId=Bkxi11nsdr). Perhaps, the experience replay part is somewhat novel. It seems emphasizing more that the aim is to show that simple methods are competitive rather than focusing on novelty could be a good idea. Provided that the authors incorporate the feedback of the other reviewers and update the paper accordingly, it will make a good contribution.