- #SCREEPS FILTER STRUCTURES INSTALL#
- #SCREEPS FILTER STRUCTURES CODE#
- #SCREEPS FILTER STRUCTURES SIMULATOR#
#SCREEPS FILTER STRUCTURES SIMULATOR#
Grunt run: Run the bot in a fake and incomplete simulator
#SCREEPS FILTER STRUCTURES CODE#
If done properly, this should push the code to succesfully.In a terminal open in a copy of this project: type grunt deploy.Open settings.js in an editor (if the file is not available, run grunt in console), fill in the options and save the file.
#SCREEPS FILTER STRUCTURES INSTALL#
Then run npm install to install dependencies. Make now sure the terminal is pointing to this project.If you are familiar with git, you can clone from Otherwise you can get the latest code from github.Open a terminal and install grunt with npm like this: npm install -g grunt-cli.Make sure node 2.4 or later is installed.Go to or you could try 🍕 💩.ws/ 🐯 🔮 👊 🍋 😜 🐱 🍩 🐰 Install checklists First install But we may revisit this later.The main goal of this project is to provide a flexible framework for the ai. Six months may be a bit excessive, but working on something else - trying a different programming paradigm - may show you some things you’re overcomplicating.īut I’m leaving the article as is (with this caution) because it’s the approach that I ended up with. Taking a break is a valuable way to get perspective on your work. I dusted off my codebase, particularly the changes described here, which were left in an incomplete state. My second son was born in late November, and Screeps had been sidelined as I balanced first the demands of a newborn and then another side project (which I may write about eventually). I am writing this section six months after drafting the above article. get () methods) means we could about easily do something with functions:Ĭlass RoomPlans Conclusion, and a Caution The fact that we are using Maps as indexes (with their cumbersome. The object-oriented approach is not the only way to maintain a central state, and it’s possible that it’s the wrong tool for the job. In order to move quickly and test thoroughly, I need to have consistent, reliable startups. The complexity and fragility of the caching system means it’s routinely breaking on me when I respawn on my private test server. This becomes particularly acute because I am trying to test room planning and expansion. Refreshing data involves more hackery, in which we get each property without actually using it, and Sonar rightly complains about this. We are going to a lot of effort to convince Typescript of the types of our properties, and it turns out we’ve left some holes, especially when objects are just being created. This in turn makes debugging more difficult. Decorators are fancy, but this pattern probably isn’t intuitive for other developers, and it hides a convoluted mess. Caching becomes a web of logic, dependent upon non-standard patterns. We can get or set properties from any part of the state tree and trust that they will be properly preserved across ticks or global resets. In a previous article we built an object-oriented cache of our WorldState, using decorators and custom property definitions to create dynamic classes that can transparently get data from game objects and cache it either in heap or Memory in case vision is lost.Īfter a bit of setup, this makes interacting with the world state easy. See the Conclusion section for some additional perspective on these changes. Note: This article was drafted back in November. Here’s the GitHub repo if you’d like to follow along. The article below describes the AI in its current state: I’m still expanding and refining my codebase.
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