Reinforcement learning on an omnidirectional mobile robot

By Roland Hafner, Martin A. Riedmiller. 2003

26 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Sergey Levine
2. Pieter Abbeel
3. Jan Peters
4. Jens Kober
5. Chelsea Finn
6. J. Bagnell
7. J. Urgen Schmidhuber
8. Martin A. Riedmiller

In Computational Theory and Mathematics

1. Sergey Levine
2. J. Bagnell
3. Jan Peters
4. Jens Kober
5. Martin A. Riedmiller
6. Pieter Abbeel
7. Michael Montemerlo
8. J. Urgen Schmidhuber

In Control and Systems Engineering

1. Jan Peters
2. Sergey Levine
3. Jens Kober
4. J. Bagnell
5. Martin A. Riedmiller
6. Pieter Abbeel
7. Gerhard Neumann
8. Marc Peter Deisenroth

Citations in the corpus

• Reinforcement learning in robotics: A survey (2013, methodology): “It used multi-layer perceptrons to learn various sub-tasks such as learning defenses, interception, position control, kicking, motor speed control, dribbling, and penalty shots (Hafner and Riedmiller, 2003; Riedmiller et al., 2009).”
• Learning to Drive a Real Car in 20 Minutes (2007, background): “Another important aspect of this paper, is, that we not need prior knowledge about the policy to be learned.”