  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.
