My objective is to publish a series of posts explaining how ECS can be leveraged to bridge video game programming and deep (reinforcement) learning. The articles are structured to progressively demonstrate how an ECS can be developed ‘from scratch’ in python and integrated with classical DRL tools, resulting in a powerful learning platform associated to complex and beautiful games.
The choise has been made to make everything full python (Linux, Windows and MacOS), even if is not reaching the 3D rendering quality obtained by C, C++ or Rust. On the other side, since integration of deep learning models will be easy, we will obtain a strong AI-oriented game framework, together with a nice experimental platform for DRL research.
The associated source code is https://github.com/ludc/gymecs - Each blog post is associated with a branch of the github repository. The main branch corresponds to the last posted article.
I am a deep (reinforcement) learning professor from Sorbonne University on a sabbatical. I worked at Criteo and spent almost 4 years at Meta (FAIR) doing academic research but also concrete projects to democratize the use of RL techniques (e.g have a look at salina which is a wonderful RL library :) ). I recently joined Ubisoft, going back to my roots since I did AI research and computer science mainly motivated by video games when I was younger. I am right now a research scientist at La Forge.
This blog only expresses my own opinions and my personal thoughts
- 14th of October, 2022: Defining a pure python ECS, why is it good for Deep Reinforcement Learning?
In this post, we give the basics of Entity Component Systems and show a simple implementation in Python. We then develop a simple 2D maze game as an example of use.
The developped Maze Game
- 20th of October, 2022: Building bridges between gymecs and openAI Gym
In this post, we show how are ECS can be casted as a gym environment, and vice-et-versa. We also discuss the advantage of the ECS w.r.t other deep RL environments libraries aka our ECS is game-centrc while classical libraries are agent-centric.
gymecs is thus much more flexible and general.
openAI gym as a game in our ECS
The Multiagent with Goal Game can be casted as a gym environment in many ways !
- November, 2022: Adding 3D rendering with raylib
We explain how ECS can be easily extended to handle 3D rendering (using the raylib library).
The previous maze with 3D rendering
- November, 2022: Using GPU for fast computations in the ECS
gymecs allows to naturraly integrate processings made on GPU to drastically speed-up computations and to speed-up game dynamics. We show how classical GPU librairies can be used in the ECS and provide an example using JAX allowin to have millions of frames per second (without rendering).
- November, 2022 (not published yet): Integrating 3D Physics in the game
We explain how 3D physics can be integrated in the ECS by using the Physx library that works on multiple CPUs and GPUs.
The use of physx in the ECS