If you haven’t heard of Twitch Plays Pokemon, you should check out that Wikipedia article to get some bacgkround so that when I reference Twitch Plays Pokemon (TPP), it makes sense. Recently I began to wonder about the possibility of experimenting with another system I grew up with, the Gamecube. The GC controller is way more complex than a Gameboy’s inputs so the choice of games was definitely limited (nothing like Smash or Monkeyball - anything that requires real-time). I decided upon using Animal Crossing, a single-player RPG that doesn’t require a lot of real-time reactions, which is perfect for the ~30s Twitch lag. I had to figure out how to build a system that converts a chat conversation into Gamecube controller inputs, and this blog will be a bit a dive into the software that makes it work.

Emulation and Controls

The first trick was to figure out how to programmatically control a Gamecube from my computer. After a little research, it appeared that the Dolphin emulator is one of the more popular and still active emulators for Wii and Gamecube games, so I decided to go with that one. To simulate controller inputs, I first began to look into python libraries for keypresses on OSX, but unfortunately all I found was some meagerly maintained solutions or direct actionscript call examples and I wasn’t too keen on going down that route. After a little more searching, I found this pull request, evidence that one of the Dolphin devs put in the ability to input controls via named pipes on unix systems. With that, I realized this idea was possible and I began building out some test code that interfaced my python program with Dolphin emulator - there was some tricky configuration with the Dolphin controller settings, but thankfully spxtr documented it pretty well.

Next up was wrangling the Twitch chat and piping that into a separate thread. Twitch allows for us to connect using IRC, and while there are more complicated python libs for IRC support, I decided to go with the simple solution, the stdlib’s socket interface. There were only a couple gotchas here, one being that Twitch IRC now requires an oauth token and that every ~5 minutes or so, Twitch sends out a PING message that you need to respond to with a PONG so that it keeps the connection open. For the first one, I just ended up using a third party oauth token generator that handled the oauth flow and spit out a token for me to use. The second one isn’t hard either, but if it slips past you when reading the documentation, you’ll have some potentially very tricky bugs once your code runs for a little while.

Once those individual pieces were figured out, I had to somehow glue them together. I wanted the system to be able to handle a large load of chat messages without affecting the rate at which the controller moved, meaning I wanted the controller to move only once every given interval. I also wanted to be able to plug in another data source instead of a Twitch chat (for example.. Twitter maybe?) so I tried to write the code to be as de-coupled as possible. Because of this, I took advantage of the Process class from the multiprocessing library, built into Python. At a high level, there was a TwitchStream class that extended Process to run in a separate process from the main controller. This class handled all incoming messages, processed them accordingly and then put them on the MessageProcessors message buffer, a FIFO queue with a max size. As messages come in from the TwitchStream in real-time, they get put on this queue, where every third of a second a message will get pulled off and sent to the controller. If more messages come in than the size of the FIFO queue allows, old messages will get tossed, and the controller will choose the oldest message in that queue. The reason for one more layer of separation here is to allow for future selection strategies, such as the average of all commands in the last .4s, etc. I wanted the controller to have as little knowledge at all of how messages were converted to controls so that in the future the MessageProcessor can be modified without affecting the actual controller code. You can take a look at the below diagram for some more details:


Doing it live!

Once the basic structure was working, I decided to start streaming as soon as I could to see what I would learn from trying it out. When I first started streaming to Twitch, the only thing showing was the GameCube screen, other than the chat, there wasn’t much context for what was going on. After letting it go for a few hours, the majority of people came, tried a few inputs, and then left. There were others that were more dedicated and had specific goals, such as getting the day to be halloween so that they could try to get Halloween specials. Because the twitch chat isn’t persistent, I decided to put some sort of scoreboard so that passing visitors could see who was contributing, and that they could see their inputs showing up when they typed text into the chat.

I built a small node app that runs alongside the controller code that would listen on an endpoint to update a smiple UI that displayed top contributors, latest inputs, etc. The controller would send requests every few seconds to update the leaderboard so that it was pseudo-realtime. After redeploying this and running it another weekend, you could see that the majority of people contributed anywhere from 3- 30 commands total, but some would dedicate large amounts of time, ranging from 100 to 300 inputs for the game.

One of the biggest challenges I didn’t anticipate was dealing with the 30s lag. Like I mentioned above, Animal Crossing is a game that mostly doesn’t require real-time reactions - one of the biggest challenges was trying to talk to some of the villagers that would literally run in circles around our character that was always moving in the wrong direction because of the lag.

What’s Next

After spending a solid couple of weeks working on this, I’m going to shelve some new changes for a little while once I get it running 100% of the time on an Ubuntu box I’m currently setting up. Up til now, it’s been running on a beefy MacbookPro when I don’t need it, but that’s not a great long-term solution. Since my programming solution requires a UNIX-based system, I don’t have the choice to do it on the Windows desktop I have at home without dual-booting Ubuntu.

One of the other big fixes I want to add is stats persistence on startup and shutdown. Right now if Dolphin crashes or something similar, I restart the python application, it doesn’t save state of the current users and history. Unfortunately, this happens more than I would like right now, so I need to be able to save to disk and reload on startup. I’m probably going to use some sort of yaml solution since I’ve been on a yaml tear lately.

Next up is community-sourced goal-setting. For the random passerby in the chat, they won’t have any context about what is actually going on or what they should try to push the character towards. I originally thought about just adding a goal that I could update whenever I wanted in the node app, but I realized that would get out of date really fast and wouldn’t reflect the current state of the game. Adding a custom command that would allow for users’ to set this could potentially turn out horribly but it could also be very useful, so it’ll be the next little experiment I try.

If you’ve made it this far, thanks for reading! Building this was a lot of fun and I learned a lot from much of the poor multi-threaded application code I wrote, but it was great practice. If you’re interested in reading some of the code I wrote for this, you can check it out on github here. If you want to see the actual stream, it’s hosted on twitch of course, and it’s found here. If you have any comments, ideas, or suggestions, feel free to open an issue on Github or email me!