This is a summary of Julius von Kügelgen’s presentation titled “Causality for Machine Learning.” In his talk, he explored the mathematical concepts behind causality and how they differ from traditi...

Introduction In this blog post, I will delve into the paper titled “Toward Causal Representation Learning” (2021) (link). The primary objective of this paper is to address open challenges in machi...

Introduction Expanding on our previous discussions about Proximal Policy Optimization (PPO) in reinforcement learning, let’s now focus on a crucial aspect: hyperparameter optimization. In reinforc...

Embarking on the exploration of deep reinforcement learning involves taking a closer look at algorithms that guide the training of smart agents in complex environments. This blog post aims to exami...

Robot Description The Fetch Robot is a mobile manipulator designed for human working environments (indoor). It has 13 DoF in total, with a mobile base, torso lift, 7-DoF arm, gripper and head. Fet...

The choice of simulator for Reinforcement Learning is not trivial. There are multiple characteristics each simulator possess that could influence this choice, and some details are far to complex to...

Introduction to Reinforcement Learning The objective in Reinforcement Learning is to find ways for an agent to learn to make good sequences of decisions. We indicate to the agent which action is g...