Using DDPG agent to control UAV system with energy efficiency - GitHub - ducmngx/DDPG-UAV-Efficiency: Using DDPG agent to control UAV system with energy efficiency
DDPG算法是一种基于DNN的无模型的 off-policy actor - critic算法,可以学习连续动作空间中的策略。该算法由策略函数和 q 值函数组成。策略函数扮演一个参与者来生成动作。q 值函数作为一个批评家,评价行为人的表现,并指导行为人的后续行动。DDPG 算法的整个训练过程可以总结如下:首先,演员网络 \mu 在上一个训练...
The digital twin functionalities can be implemented as a part of the GBS management software, conducting the ray-tracing and DDPG position optimization procedure to determine the ABS position. Only one UAV ABS is assumed to be used within the target area of interest, and the wireless backhaul ...
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. ...
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 ...
4, the main modules in the framework of DDPG are the environment, experience pool, actor network and critic network. The functions of the environment and experience pool are used to generate and store experience, respectively. In the process of continuously interacting with the environment, the ...
However, when the number of UEs reached 200, DDPG outperformed HJPQ by a significant margin, owing to the delay constraints. Overall, the HJPQ algorithm had the upper hand as it had lower computational complexity. 7. Comparison of offloading algorithms 7.1. Performance comparison criteria In ...
DDPG -Handles continuous action spaces;-Suitable for high-dimensional problems;-Learns from direct interaction with the environment. -Sensitive to hyperparameter choices;-Requires large amounts of training data;-May lack stability in real-world implementation. [29] DQN -Learns directly from the enviro...
Obtaining optimal hovering position, offloading ratio, and flight path with deep reinforcement learning algorithms like PPO, DDPG, and SAC is challenging. Thus, we combine SAC with A* to propose the A*SAC algorithm for solving these issues. 4.1. Markov Decision Process The A*SAC algorithm is ...
In this section, we employ the DDPG method with the DIMDP definitions, and then a model-based DIDDPG algorithm for the continuous UAV formation control is proposed. The framework of DIDDPG is presented as in Figure 5. The main network includes two parts (i.e., critic network and actor ...