Reinforcement Learning

The CES-IA Reinforcement learning class slides.

Beginner tutorial on Stable Baselines library with colab notebooks

The presentation of the 2019 ICINCO paper

This paper proposes a method for dynamically varying the gains of a mobile robot controller that takes into account, not only errors to the reference trajectory but also the uncertainty in the localisation. To do this, the covariance matrix of a …

Scaling end-to-end reinforcement learning to control real robots from vision presents a series of challenges, in particular in terms of sample efficiency. Against end-to-end learning, state representation learning can help learn a compact, efficient …

State representation learning aims at learning compact representations from raw observations in robotics and control applications. Approaches used for this objective are auto-encoders, learning forward models, inverse dynamics or learning using …

Reinforcement learning library: a fork of OpenAI Baselines

S-RL Toolbox: Reinforcement Learning (RL) and State Representation Learning (SRL) for Robotics