AssetStudio VS ml-agents

Asset Studio for browsing and previewing files, Avalonia for replacing/editing bundles. Of course, I've never actually modded RF5; all I've managed to get working so far for RF3s is portrait and model texture editing, and face textures are so tightly cropped that replacing them might be a pain. As for dialogue, the extracted .txt comes out garbled, and I tried it with Grimoire just for fun but that doesn't seem to do anything.

Tile graphics are available as-is in the game folder (Shadowrun Hong Kong\SRHK_Data\StreamingAssets\ContentPacks etc). For the rest, use tools for ripping stuff from unity games - UABE, AssetStudio etc to open the resources.assets files.

https://github.com/AssetRipper/AssetRipper and https://github.com/Perfare/AssetStudio, depends on what you want. If yoy want to rip an entire scene or gameobjects/prefabs, use AssetRipper. Else if you just want sounds, models, animations (from FBX objects), textures, fonts and this kind of stuff, AssetStudio works

AssetStudio - let's you extract models, textures, sounds, etc. from the game's assetbundles (aka: the game's compressed / packed data). There's also this guide on other programs for Unity modding - less of a guide really, more for finding out what other tools there are.

Something like AssetStudio so I can extract music from Unity games. Wine works perfectly fine, though I'd rather have a native build of it.

https://github.com/Perfare/AssetStudio/releases/download/v0.16.47/AssetStudio.net472.v0.16.47.zip Here's the direct download link, just extract then run the .exe

and PR content: https://github.com/Unity-Technologies/ml-agents/commit/ed212103e451449bf84711a4a8f7bf11dfb1211a

Thanks! It really depends on the task that you want to implement. But in general, sticking to the standard gymnasium API is important. If you want to implement a 2D environment then PyGame is promising. If it's more like a game, check out Unity ML-Agents or Godot RL Agents. Anything simpler can also be just pure python code. You also need to carefully design your observation space, action space and reward function. My advice is to explore design choices of related environments.

When looking into implementing RL in a game environment, I found that both Unity MLAgents and the third-party UnrealCV communicate between the game environments and Python using sockets. I am looking into implementing RL for Unreal and wondering about the performance impact of using sockets vs using RL C++ libraries to keep everything "in-engine"/native.

For the last question, I suggest downloading this example package and taking a look at the Soccer example. It shows how to have 2 completely different Agents on different teams learn from each other.

What helped me the most to get started was this youtube video, and then after that I would recommend going through the official unity github examples and their scenes to understand how they approached different tasks.

# Install steps - download the `ml-agents` repository `git clone https://github.com/Unity-Technologies/ml-agents` - create a Python folder in `ml-agents` and clone `social_rl` repo into it `svn export https://github.com/google-research/google-research/trunk/social_rl` - copy `environments.py` and `gymwrappers.py` into this Python folder - create a python3.8 environment and install `social_rl` requirements `conda create -n mlagents python=3.8` `pip install -r requirements.txt` - install `ml-agents_envs`, `ml-agents` and `gym-unity` from the `ml-agents` repository `python install setup.py`

Unity ML-Agents is a relatively new add-on to the Unity game engine. It allows game developers to train intelligent NPCs for games and enables researchers to create graphics- and physics-rich RL environments. Project ideas to explore include:

Descriptions of the configurations are available in the ML-Agents official documentation.

Inspired by Slime Volleyball Gym, I built a 3D Volleyball environment using Unity's ML-Agents toolkit. The full project is open-source and available at: 🏐 Ultimate Volleyball.