Offline Reinforcement Learning for Quadrotor Control: Overcoming the Ground Effect
Luca Sacchetto,Mathias Korte,Sven Gronauer,Matthias Kissel,Klaus Diepold,Luca Sacchetto,Mathias Korte,Sven Gronauer,Matthias Kissel,Klaus Diepold
Applying Reinforcement Learning to solve real-world optimization problems presents significant challenges because of the large amount of data normally required. A popular solution is to train the algorithms in a simulation and transfer the weights to the real system. However, sim-to-real approaches are prone to fail when the Reality Gap is too big, e.g. in robotic systems with complex and non-line...
