Sampling-based Inverse Reinforcement Learning Algorithms with Safety Constraints
Johannes Fischer,Christoph Eyberg,Moritz Werling,Martin Lauer,Johannes Fischer,Christoph Eyberg,Moritz Werling,Martin Lauer
Planning for robotic systems is frequently formulated as an optimization problem. Instead of manually tweaking the parameters of the cost function, they can be learned from human demonstrations by Inverse Reinforcement Learning (IRL). Common IRL approaches employ a maximum entropy trajectory distribution that can be learned with soft reinforcement learning, where the reward maximization is regular...
