… RL has been effectively applied in many important areas of real life. This article intends to
provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. …

… Also within the formalism of optimal control one can give up optimality and, instead,
design controllers that are more robust to mismatches between model and reallife, namely by …

… autonomous learning. We give a summary of the state-of-the-art of reinforcement learning in
… Three recent examples for the application of reinforcement learning to real-world robots are …

… He is passionate about popularizing artificial intelligence technologies and established
TensorLayer, a deep learning and reinforcement learning library for scientists and engineers, …

In this paper, we introduce the first learning-based planner to drive a car in dense, urban traffic
using Inverse Reinforcement Learning (IRL). Our planner, DriveIRL, generates a diverse …

… Identification and definition of real-world challenges: Our main goal is to more clearly
define the issues reinforcement learning is having when dealing with real systems. By making …

… learn from feedback via unimodal or multimodal sensory input. This paper reviews methods
for interactive reinforcement learning agent to learn … human-human interaction in the real life. …

… games to solving complex real-life problems in autonomous … selecting the appropriate
reinforcement learning algorithm that … and classify reinforcement learning algorithms according to …

… how inverse reinforcement learning (IRL) can be employed to retrieve the reward
function implicitly optimized by expert agents acting in real applications. Scaling IRL to real-world …

… REINFORCEMENT LEARNING FOR REAL LIFE We organized the ICML 2019 Workshop on
Reinforcement Learning for Real Life … theoretical issues in applying RL to real life scenarios. …