D3QN(Dueling Double DQN)则是结合了Dueling DQN和Double DQN的优点。 1. Dueling DQN 决斗(Dueling)DQN,网络结构如图1所示,图1中上面的网络为传统的DQN网络。图1中下面的网络则是Dueling DQN网络。Dueling DQN网络与传统的DQN网络结构的区别在于Dueling DQN的网络中间隐藏层分别输出value函数$V$和advantage function优...
Compared with the classical algorithm [e.g., Rapidly-exploring Random Trees Star (RRT*), DQN, and D3QN], with simulation experiments conducted in realistic terrain and ocean currents, the proposed ND3QN algorithm demonstrates the outstanding characteristics of a higher success rate of AUV path ...
For example, DDQN [6], a target network is added on the basis of DQN, which can reduce overestimation to some extent. D3QN uses Dueling Network [7] architecture on the basis of DDQN. It uses the network to express two estimators, namely the state value function and the action advantage ...
The first two figures show that all models learn to solve Pong with D3QN with Prio and DQN being the faster learners. The third figure shows that DQN initially learns that the average Q-value is negative while playing randomly. After some time, the average Q-values increase along with the ...
decision processes (POMDPs) and solved using two methods: a deep Q-network (DQN) and dueling double deep Q-Network (D3QN) to achieve the optimal ... AH Zarif,P Azmi,N Mokari,... 被引量: 0发表: 2021年 加载更多站内活动 0关于...
强化学习DQN算法 DQN概述 DQN简述 DQN算法主要的算法流程是将神经网络与Q-learning算法结合。利用神经网络强大的表征能力,将高维的输入数据作为强化学习中的state,作为神经网络模型(Agent)的输入; 随后神经网络模型输出每个动作对应的价值(Q值),得到将要执行的动作。强化学习的目标是通过学习从而获得最大的奖励。 接...
D3QN(Dueling Double DQN)。Dueling DQN 与Double DQN 相互兼容,一起用效果很好。简单,泛用,没有使用禁忌。 在论文中使用了D3QN应该引用DuelingDQN 与 DoubleDQN的文章。 只需将DuelingDQN中的loss计算方式修改为DoubleDQN的方式即可。 # Epsilon_Greedy_Exploration # MAX_Greedy_Update class Dueling_DQN: def _...
For example, DDQN [6], a target network is added on the basis of DQN, which can reduce overestimation to some extent. D3QN uses Dueling Network [7] architecture on the basis of DDQN. It uses the network to express two estimators, namely the state value function and the action advantage ...
Compared with the classical algorithm [e.g., Rapidly-exploring Random Trees Star (RRT*), DQN, and D3QN], with simulation experiments conducted in realistic terrain and ocean currents, the proposed ND3QN algorithm demonstrates the outstanding characteristics of a higher success rate of AUV path ...
3.6. Dueling Double DQN The Dueling Double DQN (D3QN) algorithm integrates both the Double DQN and Dueling DQN methods. Considering that both Double DQN and Dueling DQN target the intrinsic limitations of DQN to provide enhancements, it is necessary to first briefly review the basic principles of...