AirSim
ml-agents
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AirSim | ml-agents | |
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10 | 60 | |
15,748 | 16,194 | |
1.2% | 1.5% | |
0.0 | 8.1 | |
about 2 months ago | 7 days ago | |
C++ | C# | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
AirSim
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Currently writing out a plan for an RL based path-planning project. (I'm doing it for my Smart Vehicles course in my Master's Degree) Don't have much domain knowledge atm but looking for some advice on how to approach the problem?
AirSim: https://github.com/microsoft/AirSim
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8+ Reinforcement Learning Project Ideas
AirSim
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Is it possible to train a self driving car on google colab?
I've been trying for a while now and I started thinking it may not be possible. If anyone has managed to train a self-driving car simulator using openai gym on google colab(preferably), or on any remote server (AWS, GCP, ...) please let me know. So far, I tried carla, airsim, svl, deepdrive and they are all equally useless unless run locally with a gui. I'd really appreciate if someone suggests some way that actually can make it possible.
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?
carla - Open-source simulator for autonomous driving research.
gym - A toolkit for developing and comparing reinforcement learning algorithms.
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
GAAS - GAAS is an open-source program designed for fully autonomous VTOL(a.k.a flying cars) and drones. GAAS stands for Generalized Autonomy Aviation System.
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
Autonomous-Ai-drone-scripts - State of the art autonomous navigation scripts using Ai, Computer Vision, Lidar and GPS to control an arducopter based quad copter.