Hi. I’m Louis Kirsch.
I am a PhD with Jürgen Schmidhuber at IDSIA (The Swiss AI Lab) working on Meta Reinforcement Learning agents. Previously I have been at University College London completing my Master of Research. My long-term research goal is to make RL agents learn their own learning algorithm, making them truly general in the AGI sense. They should be able to design their own abstractions for prediction, planning, and learning, and continuously learn from arbitrarily many environments. I have outlined these challenges in my blog post and I also recommend reading Jeff Clune’s view and Juergen’s legacy.
- Currently PhD student with Jürgen Schmidhuber at IDSIA (The Swiss AI Lab).
- Graduated as the best student of class 2018 from University College London supervised by David Barber (MRes Computational Statistics and Machine Learning).
- Graduated as the best student of class 2017 from Hasso Plattner Institute
HPI is ranked 1st for computer science in most categories in Germany (CHE ranking 2015)
- Self-employed software developer during high school and my undergraduate studies [Project selection]
My first work on learning RL algorithms is on ArXiv!
Improving Generalization in Meta Reinforcement Learning using Learned Objectives
Contemporary Challenges in Artificial Intelligence [PDF]
Technical report (Kirsch September 2018)
Scaling Neural Networks Through Sparsity [PDF]
Technical report (Kirsch July-October 2018)
Recent blog posts
I present an updated roadmap to AGI with four critical challenges: Continual Learning, Meta-Learning, Environments, and Scalability. I motivate the respective areas and discuss how research from NeurIPS 2018 has advanced them and where we need to go next. [Continue reading]
Reinforcement Learning promises to solve the problem of designing intelligent agents in a formal but simple framework. This blog post aims at tackling the massive quantity of approaches and challenges in Reinforcement Learning, providing an overview of the different challenges researchers are working on and the methods they devised to solve these problems. [Continue reading]
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