对于动作空间较大的任务,Deep Q-Network中的估值过高会非常严重,从而导致算法无法达到预期。 Double Deep Q-Network核心思想 为了解决上述问题,Double Deep Q-Network(简称DDQN)被提出,其核心思想是使用两个独立训练的神经网络来分别选择动作和评估价值。具体来说,在计算目标网络的Q值时,我们不再直接使用贪心策略
A two-stage double deep Q-network, TS-DDQN algorithm is proposed based on an improved deep reinforcement learning algorithm to solve the complex multi-objective DFJSP optimization problem by adopting two-stage decision-making. In the TS-DDQN, an external nondominated set is used to enhance the...
Double Deep Q-Network (DDQN) 是一种用于强化学习中的深度学习算法,特别是在处理离散动作空间的 Q-Learning 问题时非常有效。DDQN 是对传统 Deep Q-Network (DQN) 的一种改进,旨在解决 DQN 在估计 Q 值时可能存在的过高估计(overestimation)问题。 DDQN 使用一个额外的神经网络来评估选取最大 Q 值的动作。它...
The EV charging scheduling problem is formulated as a MDP problem, and it is solved by a state of the art DRL algorithm named Double Deep Q-Network and Prioritized Experience Replay (DDQN-PER). Firstly, three feature extraction networks based on Long Short Time Memory (LSTM) are used to ...
Double Deep Q-Network (DDQN) 是一种用于强化学习中的深度学习算法,特别是在处理离散动作空间的 Q-Learning 问题时非常有效。DDQN 是对传统 Deep Q-Network (DQN) 的一种改进,旨在解决 DQN 在估计 Q 值时可能存在的过高估计(overe...
深度强化学习可以将深度学习与强化学习相结合:深度学习擅长从原始数据中学习复杂的表示,强化学习则使代理能够通过反复试验在给定环境中学习最佳动作。通过DRL,研究人员和投资者可以开发能够分析历史数据的模型,理解复杂的市场动态,并对股票购买、销售或持有做出明智的决策。
Double Deep Q-Network (DDQN) 是一种用于强化学习中的深度学习算法,特别是在处理离散动作空间的 Q-Learning 问题时非常有效。DDQN 是对传统 Deep Q-Network (DQN) 的一种改进,旨在解决 DQN 在估计 Q 值时可能存在的过高估计(overestimation)问题。
the dueling-double-deep Q-network with twin state-value and action-advantage functions (D3QNTF). First, dual action-advantage and state-value functions are used to prevent overestimation of action values. Second, a method for random initialization of feasible solutions improves sample quality early ...
Although the existing deep Q network (DQN) algorithm in Deep reinforcement learning can solve the problem of real-time updating, its prediction results are always higher than the actual results. In Botnet traffic detection, although it performs well in the training set, the accuracy rate of ...
To address this problem, this paper proposes a control strategy based on the Double-DQN (Double Deep Q-Network) [20] algorithm. Double DQN is an extended version of DQN aimed at solving the problem of overestimation when estimating Q values. The principle of Double DQN is to reduce the imp...