Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Matiisen, Tambet"'
So-called implicit behavioral cloning with energy-based models has shown promising results in robotic manipulation tasks. We tested if the method's advantages carry on to controlling the steering of a real self-driving car with an end-to-end driving
Externí odkaz:
http://arxiv.org/abs/2301.12264
The core task of any autonomous driving system is to transform sensory inputs into driving commands. In end-to-end driving, this is achieved via a neural network, with one or multiple cameras as the most commonly used input and low-level driving comm
Externí odkaz:
http://arxiv.org/abs/2206.15170
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems, 2020
Autonomous driving is of great interest to industry and academia alike. The use of machine learning approaches for autonomous driving has long been studied, but mostly in the context of perception. In this paper we take a deeper look on the so called
Externí odkaz:
http://arxiv.org/abs/2003.06404
Perspective taking is the ability to take the point of view of another agent. This skill is not unique to humans as it is also displayed by other animals like chimpanzees. It is an essential ability for social interactions, including efficient cooper
Externí odkaz:
http://arxiv.org/abs/1907.01851
Assisted by neural networks, reinforcement learning agents have been able to solve increasingly complex tasks over the last years. The simulation environment in which the agents interact is an essential component in any reinforcement learning problem
Externí odkaz:
http://arxiv.org/abs/1808.10692
Inferring other agents' mental states such as their knowledge, beliefs and intentions is thought to be essential for effective interactions with other agents. Recently, multiagent systems trained via deep reinforcement learning have been shown to suc
Externí odkaz:
http://arxiv.org/abs/1805.06020
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. We desc
Externí odkaz:
http://arxiv.org/abs/1707.00183
Autor:
Tampuu, Ardi, Matiisen, Tambet, Kodelja, Dorian, Kuzovkin, Ilya, Korjus, Kristjan, Aru, Juhan, Aru, Jaan, Vicente, Raul
Multiagent systems appear in most social, economical, and political situations. 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
Externí odkaz:
http://arxiv.org/abs/1511.08779
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