Flexible Heuristic Dynamic Programming for Reinforcement Learning in Quadrotors

Reinforcement learning is a paradigm for learning decision-making tasks from interaction with the environment. Function approximators solve a part of the curse of dimensionality when learning in high-dimensional state and/or action spaces. It can be a time-consuming process to learn a good policy in a high dimensional state space directly. A...