Search Results for author: Tambet Matiisen

Found 7 papers, 4 papers with code

LiDAR-as-Camera for End-to-End Driving

no code implementations30 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.

Autonomous Driving

Perspective Taking in Deep Reinforcement Learning Agents

no code implementations3 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.

reinforcement-learning

APES: a Python toolbox for simulating reinforcement learning environments

2 code implementations31 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.

reinforcement-learning

Do deep reinforcement learning agents model intentions?

1 code implementation15 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.

reinforcement-learning

Teacher-Student Curriculum Learning

3 code implementations1 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.

Multiagent Cooperation and Competition with Deep Reinforcement Learning

4 code implementations27 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.

Q-Learning reinforcement-learning

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