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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better ml-agents alternative or higher similarity.
ml-agents reviews and mentions
Posts with mentions or reviews of ml-agents.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-08.
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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.
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Stats
Basic ml-agents repo stats
59
15,390
7.1
19 days ago
Unity-Technologies/ml-agents is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
The primary programming language of ml-agents is C#.
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