Probabilistic Modeling for Fast Autonomous Learning


Reinforcement learning (RL) is a mathematical framework for learning from trial and error, which makes it an appealing framework for intelligent systems and autonomous learning. RL has had many success stories recently, but it is typically data hungry. In many practical situations, however, we are faced with the challenge of making decisions based on small datasets and limited experience. In this talk, I will outline approaches based on probabilistic modeling and Bayesian inference to tackle this problem.

Key references

  • Marc P. Deisenroth, Dieter Fox, Carl E. Rasmussen, Gaussian Processes for Data-Efficient Learning in Robotics and Control, IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 37, pp. 408–423, 2015
  • Sanket Kamthe, Marc P. Deisenroth, Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control, Proceedings of the International the Conference on Artificial Intelligence and Statistics (AISTATS), 2018
  • Steindór Sæmundsson, Katja Hofmann, Marc P. Deisenroth, Meta Reinforcement Learning with Latent Variable Gaussian Processes, Proceedings of the International the Conference on Uncertainty in Artificial Intelligence (UAI), 2018
  • Date
    University of Nairobi, Kenya
    Marc Deisenroth
    DeepMind Chair in Artificial Intelligence