マルチエージェント・クアッドコプター制御の強化学習のためのPyBullet物理演算Gym環境
Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control
Bullet物理エンジンを用いた複数クアッドコプター向けのOpenAI Gym風オープンソース環境を提案し、マルチエージェント・視覚ベースの強化学習インターフェースや衝突・空力効果を備え、制御と強化学習の両面で応用例を示した。
著者: Jacopo Panerati, Hehui Zheng, SiQi Zhou, James Xu, Amanda Prorok, Angela P. Schoellig
分類: cs.RO, cs.LG
原文アブストラクト
Robotic simulators are crucial for academic research and education as well as the development of safety-critical applications. Reinforcement learning environments -- simple simulations coupled with a problem specification in the form of a reward function -- are also important to standardize the development (and benchmarking) of learning algorithms. Yet, full-scale simulators typically lack portability and parallelizability. Vice versa, many reinforcement learning environments trade-off realism for high sample throughputs in toy-like problems. While public data sets have greatly benefited deep learning and computer vision, we still lack the software tools to simultaneously develop -- and fairly compare -- control theory and reinforcement learning approaches. In this paper, we propose an open-source OpenAI Gym-like environment for multiple quadcopters based on the Bullet physics engine. Its multi-agent and vision based reinforcement learning interfaces, as well as the support of realistic collisions and aerodynamic effects, make it, to the best of our knowledge, a first of its kind. We demonstrate its use through several examples, either for control (trajectory tracking with PID control, multi-robot flight with downwash, etc.) or reinforcement learning (single and multi-agent stabilization tasks), hoping to inspire future research that combines control theory and machine learning.
