Obedience Trainer Pro 2013
June #1GAM Dog Obedience training simulator.
Back of the box
Welcome to Obedience Trainer Pro 2013!
Take on the role of an obedience trainer and train those dogs up.
Text in brackets, like [SPACE], represents buttons that perform an action.
At a basic level, you are taking a dog for a walk through its neighborhood.
You’ll encounter stimuli along the way (dogs, cats, people, etc.). If you wait
close to the stimulus, the dog will interact with it.
During the interaction, you’ll be faced with the option to reward or correct
behaviors that the dog exhibits. Do this to teach the dog to behave properly.
Each training session is only 20 turns (on the map), so work quickly.
Take dogs for a walk around the neighborhood
When you encounter stimuli, wait for them to interact
Reward good behavior, Correct bad behavior
Become the ultimate trainer!
Click Play to start
WASD – Walk around the neighborhood
Space – Continue/Wait
Why Dog Training?
Dogs and dog training is a bit of a passion of mine, and I’ve been super interested in how I’d approach AI, so this was a good fit.
Where are the graphics?
Good question. I really dove deep on the logic behind interactions, and coupled with a busy month at work, didn’t have time to add them.
Ultimately, I decided to wrap the whole thing as a text based adventure like those of yore. I think it turned out well.
What else could you do with it?
I really wanted to add progression for the trainer and more variety. I’d love more scenarios than just encounters to train the dog in as well.
Did anything not make it in?
I spent the first week working on a 2d city generator. I was very happy with the results, but it didn’t really add anything to the idea. I scrapped it for the text-based adventure style city generator that’s in there now.
Why fewer updates this month?
I went dark for a decent part of the month while building this one
Work has been crazy, and while I was hoping to go low to high, it just didn’t happen.
As I said last month – going forward, I’m going to try to take a low-to-high approach and get playable earlier