Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces
Nicolò Botteghi,Khaled Alaa,Mannes Poel,Beril Sirmacek,Christoph Brune,Abeje Mersha,Stefano Stramigioli,Nicolò Botteghi,Khaled Alaa,Mannes Poel,Beril Sirmacek,Christoph Brune,Abeje Mersha,Stefano Stramigioli
Reinforcement learning algorithms have proven to be capable of solving complicated robotics tasks in an end-to-end fashion without any need for hand-crafted features or policies. Especially in the context of robotics, in which the cost of real-world data is usually extremely high, Reinforcement Learning solutions achieving high sample efficiency are needed. In this paper, we propose a framework co...
