Comment by itzikkatz

Open in a window
Comment

Comment by itzikkatz

This small learning engine is part of a larger project aimed at creating the ultimate Clash Royale bot. The reason I started the project is actually quite funny: my brother Duddy is a pro at the game, and since I couldn't beat him myself, I launched this initiative. Building an engine that could train directly on the actual game was impossible; the game lacks an API and offers no way to speed up gameplay. Consequently, I had to build a complete game simulator that runs faster, making it feasible to train a bot on it.

The link provided points to a "mini" version utilizing components I developed for the project. This version features just one attacking card and one defensive card. You begin by running a series of defensive sequences to see how well you can hold off the attack; then, you let the bot train from scratch and attempt to defend against the attack as effectively as possible. This is a very primitive version of the neural network; while the actual version contains two million parameters, this one has only a few thousand.

As for the project's current status, the simulator is very solid, but I haven't managed to produce a decent bot capable of playing at a human level, let alone at my brother's level. I am not a machine learning expert; I only possess basic knowledge from my CS degree. I received coding assistance from Claude, who acted as my partner in pair programming. Additionally, the game's simulation engine was built primarily by my friend Ambash, who is the project's second major contributor.

The project is MIT Licensed. I would really appreciate some help. Currently, the build process only works on Windows, not Linux. I would also love for someone with extensive machine learning knowledge to review the training and learning mechanism; I’m unsure of its quality right now and whether I’ve made fundamental errors that are ruining the training process every time.

https://github.com/itzik123/ClashRoyaleAi

Replies itzikkatz · 14h
Open on HN
Loading the discussion…

Domain filters

Stories from these domains are hidden from every list. Subdomains match too: blocking substack.com also hides danluu.substack.com. The list is kept in this browser only.

Help

Keyboard

j / k
Move down and up the story list. The arrow keys scroll whatever has focus.
Enter
Open the marked story in a window.
]
Open the next story in the list in place of the one in front. Back returns to it.
p
Pin or unpin the marked story, which keeps it in Pinned.
n / N
Move to the next or previous top-level comment in the window in front.
c
Collapse or expand that comment.
f
Hide or show the story list.
Esc
Close a menu or this help.
Access key m
Go to the menu bar. Most browsers take it with Alt on Windows and Linux, and Safari with Control and Option.
?
Show this help.

Windows

Each story opens in a window holding its article above its discussion; drag the bar between them to share the room differently. A window can be moved by its title bar, resized from any edge, snapped to a half or a corner by dragging it there, maximised, or minimised to the bar at the foot of the page. Open several stories to compare them, and switch between them from that bar. A window's Next story link reads on down the list in the same window.

A link in a comment or an article to another Hacker News or Lobsters thread opens that thread in a window too. A link to a single HN comment opens the comment above its replies.

While a story's window is in front, the Story and Discussion menus in the menu bar hold its commands: pinning, Next story, sorting, collapsing every thread, jumping to the first new comment. Each window also remembers where you were in its article and discussion, so a reload, or Back to a story that Next took you past, finds your place again. Closing a window forgets it.

The whole arrangement lives in the address, so a bookmark or a shared link brings it back, and Back undoes the last change. Moving between Hacker News, Lobsters, their lists, Pinned and Find changes only the list, and leaves the windows open.

The list

The pin at the start of a row keeps the story in Pinned, and the cross at its end hides it. Pinned can be narrowed by words in the title, site or author, by source, and to the stories you haven't opened yet, and ordered by when you pinned them, by points or by comments; the filters are part of the address, so a filtered view can be bookmarked. Scroll past the end of the list to load more. Domain filters, in the View menu, hide every story from a site.

Applets

The Applets menu in the menu bar holds three tools, each a window of its own. Replies to me takes your Hacker News user name and lists the replies to your last thirty comments and stories, checking again every three minutes while it is open, and marking what is new since you last marked them read. Look up a user opens a profile on Hacker News or Lobsters, with their submissions and recent comments, as a commenter's name in any discussion does; the bar at the top of a profile looks up someone else in the same window, and Back returns to the one before. Who is hiring? filters the posts of HN's monthly hiring threads by the words you type.

They read only what the sites publish to everyone, so none of them asks for a login, and your user name stays in this browser.

Find

Find takes any link and lists every time it was submitted to Hacker News and Lobsters, so you can read each discussion of it.

About

YAVCHN never sees your Hacker News or Lobsters login. The discussion is fetched from each site's public API; to vote or reply, follow the link above the discussion, or the arrow beside a comment, to the source's own site. Pins, hidden stories, filters and layout are kept in this browser only.

Open source: github.com/paulmooreparks/yavchn. Built with PUDL.