Reinforcement Learning

CES-IA Reinforcement Learning Class

The CES-IA Reinforcement learning class slides.

RL Tutorial on Stable Baselines

Beginner tutorial on Stable Baselines library with colab notebooks

ICINCO presentation

The presentation of the 2019 ICINCO paper

Neuroevolution with CMA-ES for real-time gain tuning of a car-like robot controller

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 …

Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics

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 …

S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning

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 …

Stable Baselines

Reinforcement learning library: a fork of OpenAI Baselines

S-RL Toolbox

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