unity-libs-nuget
ml-agents
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unity-libs-nuget | ml-agents | |
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1 | 60 | |
7 | 16,194 | |
- | 1.5% | |
3.1 | 8.1 | |
9 months ago | 8 days ago | |
Batchfile | C# | |
- | GNU General Public License v3.0 or later |
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unity-libs-nuget
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I HAVE UNLIMITED POWER!!!!
The NuGet packages I publish are stripped of their actual code they're just stubs. So no copyright infringement. I made a template repository to make it easier to do this for any mono Unity game: https://github.com/Raicuparta/unity-libs-nuget
ml-agents
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At least I put effort into the AI prompt to generate some code that people can refer to, whereas you do absolutely nothing to contribute to the community.
and PR content: https://github.com/Unity-Technologies/ml-agents/commit/ed212103e451449bf84711a4a8f7bf11dfb1211a
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TransformerXL + PPO Baseline + MemoryGym
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.
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Impact of using sockets to communicate between Python and RL environment
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.
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After 8 Hours, my ML Agents learned how to work together!
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.
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I'm failing to download a repository correctly
# 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`
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8+ Reinforcement Learning Project Ideas
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:
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How to train agents to play volleyball using deep reinforcement learning
Descriptions of the configurations are available in the ML-Agents official documentation.
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🏐 Ultimate Volleyball: A 3D Volleyball environment built using Unity ML-Agents
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.
What are some alternatives?
gym - A toolkit for developing and comparing reinforcement learning algorithms.
AirSim - Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research
carla - Open-source simulator for autonomous driving research.
AssetStudio - AssetStudio is a tool for exploring, extracting and exporting assets and assetbundles.
unity-avatar-generation - A minimal example of how to use Unity's AvatarBuilder.BuildHumanAvatar API.
ultimate-volleyball - 3D RL Volleyball environment built on Unity ML-Agents
tensortrade - An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents.
recurrent-ppo-truncated-bptt - Baseline implementation of recurrent PPO using truncated BPTT
MediaPipeUnityPlugin - Unity plugin to run MediaPipe
google-research - Google Research
xLua - xLua is a lua programming solution for C# ( Unity, .Net, Mono) , it supports android, ios, windows, linux, osx, etc.
rlcard - Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.