User:DataNomadX/Reinforcement learning/Wiki505editor Peer Review

Peer review Complete your peer review exercise below, providing as much constructive criticism as possible. The more detailed suggestions you provide, the more useful it will be to your classmate. Make sure you consider each of the following aspects: Lead Guiding questions: Has the Lead been updated to reflect the new content added by your peer? Does the Lead include an introductory sentence that concisely and clearly describes the article's topic? Does the Lead include a brief description of the article's major sections? Does the Lead include information that is not present in the article? Is the Lead concise or is it overly detailed? Content Guiding questions: Is the content added relevant to the topic? Is the content added up-to-date? Is there content that is missing or content that does not belong? Does the article deal with one of Wikipedia's equity gaps? Does it address topics related to historically underrepresented populations or topics? Tone and Balance Guiding questions: Is the content added neutral? Are there any claims that appear heavily biased toward a particular position? Are there viewpoints that are overrepresented, or underrepresented? Does the content added attempt to persuade the reader in favor of one position or away from another? Sources and References Guiding questions: Is all new content backed up by a reliable secondary source of information? Does the content accurately reflect what the cited sources say? (You'll need to refer to the sources to check this.) Are the sources thorough - i.e. Do they reflect the available literature on the topic? Are the sources current? Are the sources written by a diverse spectrum of authors? Do they include historically marginalized individuals where possible? Are there better sources available, such as peer-reviewed articles in place of news coverage or random websites? (You may need to do some digging to answer this.) Check a few links. Do they work? Organization Guiding questions: Is the content added well-written - i.e. Is it concise, clear, and easy to read? Does the content added have any grammatical or spelling errors? Is the content added well-organized - i.e. broken down into sections that reflect the major points of the topic? Images and Media Guiding questions: If your peer added images or media Does the article include images that enhance understanding of the topic? Are images well-captioned? Do all images adhere to Wikipedia's copyright regulations? Are the images laid out in a visually appealing way? For New Articles Only If the draft you're reviewing is for a new article, consider the following in addition to the above. Does the article meet Wikipedia's Notability requirements - i.e. Is the article supported by 2-3 reliable secondary sources independent of the subject? How exhaustive is the list of sources? Does it accurately represent all available literature on the subject? Does the article follow the patterns of other similar articles - i.e. contain any necessary infoboxes, section headings, and any other features contained within similar articles? Does the article link to other articles so it is more discoverable? Overall impressions Guiding questions: Has the content added improved the overall quality of the article - i.e. Is the article more complete? What are the strengths of the content added? How can the content added be improved? Examples of good feedback A good article evaluation can take a number of forms. The most essential things are to clearly identify the biggest shortcomings, and provide specific guidance on how the article can be improved. Peer review of "Homemaking" Peer review of this article about a famous painting Additional Resources Check out the Editing Wikipedia PDF for general editing tips and suggestions.

General info

Whose work are you reviewing?

DataNomadX

Link to draft you're reviewing

Reinforcement learning - Wikipedia

Link to the current version of the article (if it exists)

User:DataNomadX/Reinforcement learning - Wikipedia

Evaluate the drafted changes

(Compose a detailed peer review here, considering each of the key aspects listed above if it is relevant. Consider the guiding questions, and check out the examples of what feedback looks like.)

• Lead - The lead is functional in that it gets the point across, but it does so with a bit of clunkiness.
  • "Reinforcement Learning (RL) is one of the areas of Machine learning that solve the issue of finding out what action is to be taken by an agent in an environment in order to attain the cumulative reward. In contrast to Supervised learning, which learns by fitting examples labeled, reinforcement learning learns by trial-and-error from feedback in the form of actions and rewards."
  • This passage mentions a "cumulative award." I understand what it is, but I have to intuit it to understand. It might be a good idea to put this in layman's terms for people who may not be as familiar with ML as a subject.
  • "solve the issue of finding out what action is to be taken by an agent in an environment" - this section sounds a bit off, mostly the underlined portion. It could be more fluid by stating first what 'kind' of agent is doing the solving, and secondly by rephrasing this in some way stating that the agent is navigating some kind of environment.
• Content - There is not a large amount of content in your draft, and it is a bit difficult for me to figure out where these independent paragraphs are supposed to go. I had to use ctrl+F shortcut to look up keywords and find out where your edits are intended to go.
  • "A few of the basic notions in reinforcement learning include state, action, reward, policy, and value functions. Two broad categories of RL approaches are model-free methods such as Q-learning and policy gradients, and model-based methods using planning with simulated future state."
  • maybe the above section could be supplemented by images or diagrams? I find that it is much easier to digest this kind of content/explanation with diagrams.
  • both the article and your draft dont touch much info on google deepmind. Even though there is an article on deepmind, maybe you can include more supplemental info like the date deepmind achieved the milestone.
• Tone/Balance - Tone is neutral and feels balanced, but could maybe elaborate on some of the opposed bits of information.
  • "Despite showing great promise, RL is marred by sample inefficiency, training instability, and generalizability across environments. Safer and more efficient reinforcement learning methods continue to be an active area of research."
  • The above passage at the end of your draft is lacking in examples. You could benefit the most from linking examples of other safer/efficient learning methods.
• Sources/Reference - your sources all seem to be from reputable and academic sources. I thought it was cool that you included the Stanford cs224r class description as a real world example of the resurgence of Reinforcement learning. This class being in 2023 certainly demonstrates that. I would not have though to do this, and I will likely do something similar as an extra source.
• Organization - The sections appear to be organized, but they do not have section headers or any indication of where they need to go in the main public article.
• Images and media - elaborated on in the content section.
• Overall Thoughts - The text is clear and understandable, but can be a bit more fluid, especially in the intro area. Simple diagrams could also be a good supplement to aid in understanding of the topic. I did not feel that you were trying to sway or persuade me on whether or not RL is good or bad. All facts felt that they were presented neutrally. I think you do need to add some more content, though it seems like a difficult task given that the RL article is looks to be mostly complete.