Comparing Quadrotor Control Policies for Zero-Shot Reinforcement Learning under Uncertainty and Partial Observability
Sven Gronauer,Daniel Stümke,Klaus Diepold,Sven Gronauer,Daniel Stümke,Klaus Diepold
To alleviate the sample complexity of reinforcement learning algorithms, simulations are a common approach to train control policies before deploying the policy on a real-world robot. However, a gap between simulation and reality generally persists, which endorses the aim to train robust policies already in simulation such that those can be transferred to a real robot at a high success rate. In th...