Stabilisierung unkontrollierter Flugzustände mit Reinforcement Learning


Reinforcement learning allows a self-learning agent to stabilize an unmanned aerial vehicle in uncontrolled flight states. To achieve this, a deep deterministic policy gradient algorithm is applied. Through extensions like experience replay memory, parameterized noise, prioritized experience replay, hindsight experience replay and curriculum learning, it is furthermore possible to train environments with sparse reward.