Double DQN,通过目标Q值选择的动作来选择目标Q值,从而消除Q值过高估计的问题。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...
交通信号控制深度强化学习Dueling Double DQNDueling Network为了提高交叉口通行效率缓解交通拥堵,深入挖掘交通状态信息中所包含的深层次隐含特征信息,提出了一种基于Dueling Double DQN(D3QN)的单交叉口交通信号控制方法;构建了一个基于深度强化学习Double DQN(DDQN)的交通信号控制模型,对动作-价值函数的估计值和目标值迭代...
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 ...
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_UpdateclassDueling_DQN:def__init__...
This paper proposes an Improved Dueling Deep Double-Q Network Based on Prioritized Experience Replay (IPD3QN) to address the slow and unstable convergence of traditional Deep Q Network (DQN) algorithms in autonomous path planning of USV. Firstly, we use the deep double Q-Network to decouple the...
Figure 6. The structure of the DQN model and Dueling DQN. Figure 7. The comprehensive framework of the maneuver decision algorithm based on D3QN with EESM. Figure 8. Comparison of the learning curves with different experiment setting. The training reward is calculated as mentioned in Section 2.3...
Figure 6. The structure of the DQN model and Dueling DQN. Figure 7. The comprehensive framework of the maneuver decision algorithm based on D3QN with EESM. Figure 8. Comparison of the learning curves with different experiment setting. The training reward is calculated as mentioned in Section 2.3...
3. PER-n2D3QN Method 3.1. Double Deep Q-Network The DQN algorithm uses the maximum greedy policy to select the optimal action and estimate the target Q-value, leading to overestimation issues. The DDQN algorithm addresses this problem by using two separate Q-networks. The online Q-network ...