衝突回避を考慮したMPC強化学習によるUAV経路計画
UAV Path Planning Employing MPC- Reinforcement Learning Method Considering Collision Avoidance
LSTMネットワークを組み込んだMPCをDDPGの決定論的方策として用い、予測プールで将来の状態と行動を保持することで、複雑・不確実な環境でのUAV経路計画の収束速度と成功率を改善した。
著者: Mahya Ramezani, Hamed Habibi, Jose luis Sanchez Lopez, Holger Voos
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
In this paper, we tackle the problem of Unmanned Aerial (UA V) path planning in complex and uncertain environments by designing a Model Predictive Control (MPC), based on a Long-Short-Term Memory (LSTM) network integrated into the Deep Deterministic Policy Gradient algorithm. In the proposed solution, LSTM-MPC operates as a deterministic policy within the DDPG network, and it leverages a predicting pool to store predicted future states and actions for improved robustness and efficiency. The use of the predicting pool also enables the initialization of the critic network, leading to improved convergence speed and reduced failure rate compared to traditional reinforcement learning and deep reinforcement learning methods. The effectiveness of the proposed solution is evaluated by numerical simulations.
