Diversity is All You Need: Learning Skills without a Reward Function
Intelligent creatures can explore their environments and learn useful skills without supervision. In this paper, we propose ``Diversity is All You Need**(DIAYN), a method for learning useful skills without a reward function. Our proposed method learns skills by maximizing an information theoretic objective using a maximum entropy policy. On a variety of simulated robotic tasks, we show that this simple objective results in the unsupervised emergence of diverse skills, such as walking and jumping. In a number of reinforcement learning benchmark environments, our method is able to learn a skill that solves the benchmark task despite never receiving the true task reward. We show how pretrained skills can provide a good parameter initialization for downstream tasks, and can be composed hierarchically to solve complex, sparse reward tasks. Our results suggest that unsupervised discovery of skills can serve as an effective pretraining mechanism for overcoming challenges of exploration and data efficiency in reinforcement learning.
 *Pros:* - Mostly clear and well-written paper - Technical contribution (learning many diverse skills) is principled and well-justified (although the basic idea builds on a large body of prior work from related fields as also evidenced from the reference list) - Extensive emperical evaluation on multiple tasks and different scenarios (mainly toy examples) showing promising results. *Cons:* - The main paper assumes detailed knowledge of the actor critic setup to fully follow and appreciate the paper (a few details provided in the appendix) - p(z): it is not entirely clear to me how the dimensionality of z should be chosen in a principled manner aside from brute force evaluation (as in 4.2.2; which does not go beyond a few hundreds). What happens for many skills and would learning p(z) be preferable in this scenario? - Note: The work has been in the public domain for some time thereby limiting the apparent novelty. This has not influenced my decision as per ICLR policy. *Significance*: I think this work would be of interest to the ICLR crowd despite it having been in the public domain for a some time. It provides a simple objective for training RL models in an unsupervised manner by learning multiple diverse skills and contributes with an extensive and convincing empirical evaluation which will surely have a lasting impact in the RL subfield. *Further comments/questions*: - The authors assume that only states and not actions are observable. Intuitively, it would seem easier to obtain the desired results if the actions are also available. Could the authors perhaps clarify why it is reasonable to assume that the actions are not observable to the planner when evaluating the objective in Eq 1? Similarly, I’d like some insight into the behaviour of the proposed method if actions are also available (and how it differs from prior art in this case)? - I’d suggest enforcing consistently in the way variation across random seeds is visualised in the figures (e.g. traces in fig 4, no indication in fig5, shaped background in fig 6). - I’d suggest making it explicit what refers to in Eq. 1 (and provide some details about the SAC setup for completeness, as previously mentioned) - Minor typos etc: {p2, l6} missing word, “SAC” never defined, “DOF” never defined, and a few other typos/punctuation issues throughout.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Diversity is All You Need: Learning Skills without a Reward Function
Intelligent creatures can explore their environments and learn useful skills without supervision. In this paper, we propose ``Diversity is All You Need**(DIAYN), a method for learning useful skills without a reward function. Our proposed method learns skills by maximizing an information theoretic objective using a maximum entropy policy. On a variety of simulated robotic tasks, we show that this simple objective results in the unsupervised emergence of diverse skills, such as walking and jumping. In a number of reinforcement learning benchmark environments, our method is able to learn a skill that solves the benchmark task despite never receiving the true task reward. We show how pretrained skills can provide a good parameter initialization for downstream tasks, and can be composed hierarchically to solve complex, sparse reward tasks. Our results suggest that unsupervised discovery of skills can serve as an effective pretraining mechanism for overcoming challenges of exploration and data efficiency in reinforcement learning.
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>



 *Pros:* - Mostly clear and well-written paper - Technical contribution (learning many diverse skills) is principled and well-justified (although the basic idea builds on a large body of prior work from related fields as also evidenced from the reference list) - Extensive emperical evaluation on multiple tasks and different scenarios (mainly toy examples) showing promising results. *Cons:* - The main paper assumes detailed knowledge of the actor critic setup to fully follow and appreciate the paper (a few details provided in the appendix) - p(z): it is not entirely clear to me how the dimensionality of z should be chosen in a principled manner aside from brute force evaluation (as in 4.2.2; which does not go beyond a few hundreds). What happens for many skills and would learning p(z) be preferable in this scenario? - Note: The work has been in the public domain for some time thereby limiting the apparent novelty. This has not influenced my decision as per ICLR policy. *Significance*: I think this work would be of interest to the ICLR crowd despite it having been in the public domain for a some time. It provides a simple objective for training RL models in an unsupervised manner by learning multiple diverse skills and contributes with an extensive and convincing empirical evaluation which will surely have a lasting impact in the RL subfield. *Further comments/questions*: - The authors assume that only states and not actions are observable. Intuitively, it would seem easier to obtain the desired results if the actions are also available. Could the authors perhaps clarify why it is reasonable to assume that the actions are not observable to the planner when evaluating the objective in Eq 1? Similarly, I’d like some insight into the behaviour of the proposed method if actions are also available (and how it differs from prior art in this case)? - I’d suggest enforcing consistently in the way variation across random seeds is visualised in the figures (e.g. traces in fig 4, no indication in fig5, shaped background in fig 6). - I’d suggest making it explicit what refers to in Eq. 1 (and provide some details about the SAC setup for completeness, as previously mentioned) - Minor typos etc: {p2, l6} missing word, “SAC” never defined, “DOF” never defined, and a few other typos/punctuation issues throughout.
assistant
 *Pros:* - Mostly clear and well-written paper - Technical contribution (learning many diverse skills) is principled and well-justified (although the basic idea builds on a large body of prior work from related fields as also evidenced from the reference list) - Extensive emperical evaluation on multiple tasks and different scenarios (mainly toy examples) showing promising results. *Cons:* - The main paper assumes detailed knowledge of the actor critic setup to fully follow and appreciate the paper (a few details provided in the appendix) - p(z): it is not entirely clear to me how the dimensionality of z should be chosen in a principled manner aside from brute force evaluation (as in 4.2.2; which does not go beyond a few hundreds). What happens for many skills and would learning p(z) be preferable in this scenario? - Note: The work has been in the public domain for some time thereby limiting the apparent novelty. This has not influenced my decision as per ICLR policy. *Significance*: I think this work would be of interest to the ICLR crowd despite it having been in the public domain for a some time. It provides a simple objective for training RL models in an unsupervised manner by learning multiple diverse skills and contributes with an extensive and convincing empirical evaluation which will surely have a lasting impact in the RL subfield. *Further comments/questions*: - The authors assume that only states and not actions are observable. Intuitively, it would seem easier to obtain the desired results if the actions are also available. Could the authors perhaps clarify why it is reasonable to assume that the actions are not observable to the planner when evaluating the objective in Eq 1? Similarly, I’d like some insight into the behaviour of the proposed method if actions are also available (and how it differs from prior art in this case)? - I’d suggest enforcing consistently in the way variation across random seeds is visualised in the figures (e.g. traces in fig 4, no indication in fig5, shaped background in fig 6). - I’d suggest making it explicit what refers to in Eq. 1 (and provide some details about the SAC setup for completeness, as previously mentioned) - Minor typos etc: {p2, l6} missing word, “SAC” never defined, “DOF” never defined, and a few other typos/punctuation issues throughout.