https://github.com/mplantady/twinstickrl
Twin-stick shooter simulation using reinforcement learning in Unity
https://github.com/mplantady/twinstickrl
reinforcement-learning unityml
Last synced: about 1 month ago
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Twin-stick shooter simulation using reinforcement learning in Unity
- Host: GitHub
- URL: https://github.com/mplantady/twinstickrl
- Owner: mplantady
- License: mit
- Created: 2018-08-31T00:05:52.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2018-09-27T22:21:18.000Z (almost 8 years ago)
- Last Synced: 2025-08-13T06:37:32.254Z (12 months ago)
- Topics: reinforcement-learning, unityml
- Language: C#
- Homepage:
- Size: 52 MB
- Stars: 2
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# TwinStickRL
## Installation
UnityML is already included in the source, but you need to follow this to install the plugin TensorflowSharp:
https://github.com/llSourcell/Unity_ML_Agents/blob/master/docs/Using-TensorFlow-Sharp-in-Unity-(Experimental).md
## Environment
Inspired by the game "Geometry Wars" a ship need to survive in a small arena.
The ship can move and shoot simultaneously in any direction.
## AI Agent
The agent has 2 brains, one that control the movement and the other aim and shoot.
They both get a small reward at each step (0.1) and a negative score when the ship is killed.
## Scenes
- "Main" is a single arena to test an agent in fullscreen
- "Train" is a gym with multiple simultaneous arenas to speed-up training
## Training
You must build the game with one brain in external and the other in internal mode. (TwinMove or TwinAim)
The 2 brains are trained separately using this command:
### Move Brain
python learn.py ./build/TwinMove.exe --curriculum=./curricula/Twin_move.json --run-id=Twin_move --train
### Aim Brain
python learn.py ./build/TwinAim.exe --curriculum=./curricula/Twin_aim.json --run-id=Twin_aim --train
### Curriculum variables
To help the training some variables change over time depending on reward thresholds
- spawn_rate: number of step between 2 enemy spawns
- min_enemy_distance: minimum distance allowed to spawn
- max_enemy_count: maximum of simultaneous enemy
- reset_center_prob: probability that the agent spawn at the center of the arena
- skip_internal_brain: disable the other brain during early stage of the training
## Informations
This repository contains UnityML 0.4 (https://github.com/Unity-Technologies/ml-agents)