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
Read more about it in my blog post
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
Biological evolution has distilled the experiences of many learners into the general learning algorithms of humans. Inspired by this process, MetaGenRL distills the experiences of many complex agents to meta-learn a low-complexity neural objective function that affects how future individuals will learn. Unlike recent meta-RL algorithms, MetaGenRL can generalize to new environments that are entirely different from those used for meta-training. In some cases, it even outperforms human-engineered RL algorithms. MetaGenRL uses off-policy second-order gradients during meta-training that greatly increase its sample efficiency. [Continue reading]
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]
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