此外, DQN 算法的收敛速度远高于 DDPG 算法。 Offload-only 和 Local-only 两种算法不能充分利用整个系统的计算资源。因此,对于相同的任务大小, DDPG 算法的处理延迟明显低于 Offload-only 和 Local-only 算法。此外,随着任务大小的增大, DDPG 算法优化后的处理延迟增加速度明显慢于 Offload-only 和 Local-only 算...
Multi-Agent DDPG(MADDPG) Python API Gym Functions This Logistics Environment followsOpenAI GymAPI design : from UnityGymWrapper5 import GymEnv- import class (newest version is Wrapper5) env = GymEnv(name="path to Unity Environment", ...)- Returns wrapped environment object. ...
thus preventing overloading the UAV by excessive computing tasks from a single GU. In addition, a service fairness index is designed to assess the collective service fairness of all GUs, indicating the overall UAV service scenario. This index is synced with incremental computing...
Finally, we conduct a series of comparative experiments by comparing our UTO algorithm with the deep deterministic policy gradient (DDPG) algorithm and non-DRL algorithms to verify its performance. The main contributions of this paper can be summarized as follows: The remainder of this paper is ...
His main research interests include emerging non-volatile memory, embedded system and artificial intelligence ( ffshen@whu.edu.cn).References (46) XuX.-Y. et al. TD3-BC-PPO: Twin delayed DDPG-based and behavior cloning-enhanced proximal policy optimization for dynamic optimization affine ...
However, in practice, DDPG tends to overestimate the value function, leading to unstable training and decreased performance. TD3 addresses these issues with several key technical innovations, enhancing the stability and performance of the algorithm. The TD3 algorithm has become one of the most ...
The main work of this paper uses the multi-UAV-cooperative penetration dynamic-tracking interceptor as the scenario. Based on the deep reinforcement-learning DDPG algorithm, we establish the intelligent UAV model and realize the multi-UAV-cooperative penetration dynamic-tracking interceptor by designing ...
Once the DQN and DDPG have been trained and their performance has been validated in the simulated environment, the learned models can be deployed on the UAV’s onboard hardware during the operation phase. In this paper, our primary focus is on the training and validation aspects of the ...
Different deep reinforcement learning algorithms such as DDPG and DQN have different design methods for the action space. Due to the huge state space, using DDPG and other algorithms to train continuous maneuvering strategies will cause difficulty in neural network convergence, and the maneuver process...
The DDPG algorithm is suited for solving continuous action space problems. For the UAV landing maneuver problem, Rodriguez-Ramos et al. [121] introduced a DDPG algorithm as a solution to the problem of executing UAV landing operations on a mobile platform. Numerous simulations have been conducted...