This year we competed with 6 different solutions at the 5th edition of the AI Driving Olympics (AIDO) which was part of the 34th conference on Neural Information Processing Systems (NeurIPS). There was a total of 94 competitors with 1326 submitted solutions, thus we proudly announce that our team ranked top in 2 out of 3 challenges.

The challenge

The AI Driving Olympics is an autonomous driving challenge with the objective of evaluating the state of the art for ML/AI for embodied intelligence. …


Have you ever put your notebook under your pillow before an exam wishing that all that is written there will get consolidated into your long-term memory? I know a friend who did.

This article is about learning in dreams. More precisely desires to highlight the work of David Ha and Jürgen Schmidhuber in the field of deep reinforcement learning, sub-field model-based methods, presented at the Neural Information Processing Systems in 2018. Their paper entitled World Models demonstrates that their RL agent is able to learn by training in its own simulated environment. …


This article pursues to highlight in a non-exhaustive manner the main type of algorithms used for reinforcement learning (RL). The goal is to provide an overview of existing RL methods on a intuitive level by avoiding any deep dive into the models or the math behind it.

When it comes to explaining machine learning to those not concerned in the field, reinforcement learning is probably the easiest sub-field for this challenge. RL it’s like teaching your dog (or cat if you live your life in a challenging way) to do tricks: you provide goodies as a reward if your pet…

Robert Moni

PhD student at Budapest University of Technology and Economics; Deep Learning expert at Continental, Budapest; Interested in Reinforcement Learning.

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