Thijs Ramakers

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Real robots demonstrating online Reinforcement Learning (RL) to learn new tasks are hard to find. The specific properties and limitations of real robots have a large impact on their suitability for RL experiments. In this work, we derive the main hardware and software requirements that a RL robot should fulfill, and present our biped robot LEO that was(More)
Reinforcement Learning (RL) is a popular method in machine learning. In RL, an agent learns a policy by observing state-transitions and receiving feedback in the form of a reward signal. The learning problem can be solved by interaction with the system only, without prior knowledge of that system. However, real-time learning from interaction with the system(More)
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