no code implementations • 28 Jan 2023 • Mykyta Baliesnyi, Ardi Tampuu, Tambet Matiisen
So-called implicit behavioral cloning with energy-based models has shown promising results in robotic manipulation tasks.
no code implementations • 30 Jun 2022 • Ardi Tampuu, Romet Aidla, Jan Are van Gent, Tambet Matiisen
The core task of any autonomous driving system is to transform sensory inputs into driving commands.
no code implementations • 13 Mar 2020 • Ardi Tampuu, Maksym Semikin, Naveed Muhammad, Dmytro Fishman, Tambet Matiisen
Autonomous driving is of great interest to industry and academia alike.
no code implementations • 3 Jul 2019 • Aqeel Labash, Jaan Aru, Tambet Matiisen, Ardi Tampuu, Raul Vicente
We believe that, in the long run, building better artificial agents with perspective taking ability can help us develop artificial intelligence that is more human-like and easier to communicate with.
2 code implementations • 31 Aug 2018 • Aqeel Labash, Ardi Tampuu, Tambet Matiisen, Jaan Aru, Raul Vicente
Assisted by neural networks, reinforcement learning agents have been able to solve increasingly complex tasks over the last years.
1 code implementation • 15 May 2018 • Tambet Matiisen, Aqeel Labash, Daniel Majoral, Jaan Aru, Raul Vicente
In this work we test whether deep reinforcement learning agents explicitly represent other agents' intentions (their specific aims or goals) during a task in which the agents had to coordinate the covering of different spots in a 2D environment.
3 code implementations • 1 Jul 2017 • Tambet Matiisen, Avital Oliver, Taco Cohen, John Schulman
We propose Teacher-Student Curriculum Learning (TSCL), a framework for automatic curriculum learning, where the Student tries to learn a complex task and the Teacher automatically chooses subtasks from a given set for the Student to train on.
4 code implementations • 27 Nov 2015 • Ardi Tampuu, Tambet Matiisen, Dorian Kodelja, Ilya Kuzovkin, Kristjan Korjus, Juhan Aru, Jaan Aru, Raul Vicente
In the present work we extend the Deep Q-Learning Network architecture proposed by Google DeepMind to multiagent environments and investigate how two agents controlled by independent Deep Q-Networks interact in the classic videogame Pong.