Tic-tac-toe Evo
It is noughts that gradually will be better by learning every match.
Game info
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Game description
Android App Analysis and Review: Tic-tac-toe Evo, Developed by happyclam. Listed in Board Category. Current Version Is 1.4.5, Updated On 10/10/2018 . According to users reviews on Google Play: Tic-tac-toe Evo. Achieved Over 100 Installs. Tic-tac-toe Evo Currently Has 2 Reviews, Average Rating 5.0 Stars
Othello and chess program is sometimes referred to as the AI (artificial intelligence). Many of them choose the best move using the evaluation function, the look-ahead function, and the computing power of the computer. This way is to say brute force rather than intelligence.This program (Tictactoe Evo) will determine the move like shake the dice without the evaluation function and the look-ahead function, but would be given a reward if win, fined if lose after the match. Tic-tac-toe Evo would be choose the best move by repeating this procedure.
This is referred to as the evolutional algorithm.
- Machine Learning
- It expressed in the degree of evolution from "H. pylori" to "human beings".
- Better player, evolution will be accelerated.
- Supported one player gameplay.
- Starting first (X), starting second (O) will be determined at random.
I will not give in to such a H. pylori!
for PC: https://happyclam.github.io/tictactoe-evo/
We are currently offering version 1.4.5. This is our latest, most optimized version. It is suitable for many different devices. Free download directly apk from the Google Play Store or other versions we're hosting. Moreover, you can download without registration and no login required.
We have more than 2000+ available devices for Samsung, Xiaomi, Huawei, Oppo, Vivo, Motorola, LG, Google, OnePlus, Sony, Tablet ... with so many options, it’s easy for you to choose games or software that fit your device.
It can come in handy if there are any country restrictions or any restrictions from the side of your device on the Google App Store.
What's New
Restored how to give rewards during reinforcement learning.
