A fully custom simulation environment that interchangeably compares reinforcement learning models. Has a full blown API for modularity of algorithms, built in general genetic neural network library, and other utilities for training and testing.
- Built in python3.
- Clone this repository.
git clone https://github.com/dbinnion/AdaptableAITesting.git - To run any simulation, run
python3 main.pyinside the main directory. - Follow the terminal instructions to run any type of simulation.
Two players battle in a 2D Arena. Each player begins with 1000 Health and has one shield option to be deployed during the game that gives the character 500 more health. There are three attacks: shooting an arrow does 11.43 damage (this takes 87.5 hits to do 1000 damage), shooting a fireball does 17.77 damage (this takes 56.25 hits to do 1000 damage), and using a knife does 25 damage (this takes 40 hits to do 1000 damage).
- Freeplay Freeplay let’s you play against a character of any type.
- FSM: Finite State Machine allows you to select from three character types (range, mid,short). These are trained with profile based logic
- DC: Dynamic Controller allows you to select from three types (master,average,random). These follow logic based on a weight scheme that the player learns from the results of the game
-
Shield: If a player’s color and shape changes it means that have enabled their shield.
-
Bar below Character: Every character has a bar above below their image that displays their health percentage.
- Green = health
- Red = health lost
-
New Placement of characters: The game has ended and the next simulation has started.
-
Arrow: Long range attack
-
Fireball: Mid-range attack.
-
Knife: A Short-range attack.
| Key(s) | Description | Context |
|---|---|---|
| W, A, S, D | Movement | Player |
| LEFT, UP, RIGHT, DOWN | Directional movement | Player |
| Q | Shield | Player |
| SPACE | Long-range arrow | Player |
| E | Mid-range fireball | Player |
| R | Short-range knife | Player |
| esc | End The Game | Global |