3.2 DQN 与Dueling Network Dueling network 是一篇来自2015年的论文,这篇论文提出了一个新的网络架构,这个架构不但提高了最终效果,而且还可以和其他的算法相结合以获取更加优异的表现。 之前的DQN网络在将图片卷积获取特征之后会输入几个全连接层,经过训练直接输出在该state下各个action的价值也就是Q(s,a)。而Duelin...
本发明公开了基于D3QNPER移动机器人路径规划方法,首先进行环境建模,设计完整实验环境;利用移动机器人上的单线激光雷达对所处于当前环境进行观察,并提取出移动机器人所处于当前环境中的所有障碍物信息So;利用移动机器人运动学模型,将全局坐标系下的移动机器人自身状态信息SR,目标位置以及步骤S1.1提取的所有障碍物信息So变换...
t represents the computing resources required for computing task k in slot t; 𝑓𝑣,𝑡fv,t represents the computing resources allocated by the SDV v to compute task k in slot t, assuming that all SDVs have the same computing resources; 𝜎σ represents the energy consumption per unit ...
Each worker’s travel speed was randomly generated from 10 to 50 km/h, with a reward value set to 1 per unit of travel distance and a maximum travel distance set to 30 km. For each sensing task, the time window started from 0 to 30 and ended at 60 (in minutes). The average user...
how to compare 2 user input dates (say Date1 and Date2) in dd/mm/yyyy format, to ensure that Date2 is at least 2 months after Date1. Comparing them in days is simple but I'm not sure where to start on months, taking into account differing number of days per month and leap years...
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The UCAV is trained using the dueling double deep Q network algorithm with priority experience replay (PER-D3QN). Furthermore, the trained UCAV decision-making network is utilized to construct a zero-sum Markov game model in air combat. The optimal maneuvering strategy for both UCAVs is ...
Moreover, a Dueling double deep Q network (D3QN) is introduced to solve the MDP in a scalable and model free manner for a suitable sensor scheduling policy of UWSNs and the prioritized experience replaybuffer (PER) is utilized to improve the performance of D3QN. In addition, to ensure ...
Input: learning rate lr, batch size bs, discounting factor 𝛾γ, attenuation parameter 𝜖ϵ, attenuation rate 𝜖𝑑𝑒𝑐ϵdec, minimum attenuation value 𝜖𝑚𝑖𝑛ϵmin, target network update frequency freq, Soft update parameter 𝜏τ, maximum number of per episode 𝑛𝑡nt,...
Each worker’s travel speed was randomly generated from 10 to 50 km/h, with a reward value set to 1 per unit of travel distance and a maximum travel distance set to 30 km. For each sensing task, the time window started from 0 to 30 and ended at 60 (in minutes). The average user...