Creating huge datasets of top performing examples for Reinforcement Learning (RL) has always been tricky, but if we allow ourselves to cheat a bit it can be done very easily. During this talk, I will present a new family of algorithms that allow to efficiently generate very high quality samples for any known RL environment.
This new generation of planning algorithms achieves a performance which is several orders of magnitude higher than any other existing alternative, while offering linear time complexity and good scalability.
This talk will be a practical example of how we can use new tools for hacking any reinforcement learning environment, and make it generate superhuman level games.
Hacking RL, as any other hacking process will be divided in four phases:
1. Information Gathering
During information gathering, I will briefly explain what are the main ideas behind Reinforcement Learning. I will also talk about how our theory (FractalAI) came to be, and what are the fundamental concepts behind it.
2. Scanning and vulnerability detection
We will find an attack vector against the environment API, and explain how it can be exploited. I will explain the fundamental concepts needed to build a new generation of exploits, that will allow us to have complete control over the data the environment produces.
3.Exploitation & privilege escalation
This is the time to test the new exploits and to show a proof of concept. We will exploit the attack vector to gain access to the environment. Using only a laptop I will show how it is possible to sample data which surpasses human performance way faster than real time.
4. Maintaining access & managing tracks
Once we have gained control of the environment, we will measure how well the exploits work, and how well the techniques presented can generalize to other types of environments.
I want the talk to be as simple and fast as possible, with a lot of graphical examples, videos, and a Jupyter notebook. The Q&A session is the time to apply some social engineering to get me to talk about the details that you find more interesting. I have prepared additional material covering the most common questions and concerns, but feel free to ask whatever you want, I love challenging questions ;)
Some of the topics covered in the additional material are:
- Reinforcement Learning as a supervised problem
- Improving AlphaZero
- Hacking OpenAI baselines
- How the algorithm works
- An overview of the FractalAI repository
- Improving world models
- Combining FractalAI with neural networks
- Repository with the code of the Swarm Wave and Fractal Monte Carlo algorithms: https://github.com/FragileTheory/FractalAI
- Planning with Pixels in (Almost) Real Time
- Blind Search for Atari-Like Online Planning Revisited
- Google spreadsheet with all bencharks on Atari
- Code used to run the examples. (Not merged to the FractalAI repo yet)