We propose the Multi-agent Double Deep Q-Networks algorithm, an extension of Deep Q-Networks to the multi-agent paradigm. Two common techniques of multi-agent Q-learning are used to formally describe our proposal, and are tested in a Foraging Task and a Pursuit Game. We also demonstrate ...
As far as we know, the Multi-Agent Double Deep Q-Network (MADDQN) has not been previously studied in the literature. The study includes several steps: first, a deep reinforcement learning model based on TimesNet network for stock trading is proposed. Second, the Double DQN (DDQN) algorithm...
agent在每个episode开始时发现自己处于相同的初始状态,则在重复交互之后,接近初始状态的(s,a)的温度值更频繁地被访问时会快速衰减。但是对于lenient learners的来说,这些(s,a)的温度值仍然足够高,可以使奖励从后期传播回来,并防止agent收敛于次优策略。解决方案是将中agent可用的n个动作的平均温度折叠到正在衰减的。...
Code Issues Pull requests some Multiagent enviroment in 《Multi-agent Reinforcement Learning in Sequential Social Dilemmas》 and 《Value-Decomposition Networks For Cooperative Multi-Agent Learning》 multiagent multiagent-reinforcement-learning sequential-social-dilemmas Updated Jan 13, 2023 Python Lyapunov...
./dqn_agent.py: contains code for the implementation of DQN and its extensions (Double DQN, Dueling DQN, DQN with Prioritized Experience Replay) (See details.pdf for a detailed description of the DQN and its extensions). ./brain.py: contains code for the implementation of neural networks req...
We present DPIQN, a deep policy inference Q-network that targets multi-agent systems composed of controllable agents, collaborators, and opponents that interact with each other. We focus on one challenging issue in such systems---modeling agents with varying strategies---and propose to employ "po...
这篇文章的标题是“Computation Offloading for Multi-server Multi-access Edge Vehicular Networks: A DDQN-based Method”,发表于2024年的VTC-Spring会议,作者包括Siyu Wang、Bo Yang、Zhiwen Yu(均来自西北工业大学计算机科学学院),Xuelin Cao(来自西安电子科技大学网络空间安全学院),Yan Zhang(来自奥斯陆大学信息学部...
its action-value function using the Bellman equation, which expresses the optimal value of a state-action pair as the immediate reward plus the discounted future value of the next state-action pair. The agent iteratively updates the Q values using this equation until the optimal Q values ...
Multi-Agent Reinforcement Learning Resources Allocation Method Using Dueling Double Deep Q-Network in Vehicular Networks The communications between vehicle-to-vehicle (V2V) with high frequency, group sending, group receiving and periodic lead to serious collision of wireless ... Y Ji,Y Wang,H Zhao,...
由于单纯使用 Double Q-learning 来缓解 Q 值高估以及单纯限制梯度并不能解决 MARL 中的过估计问题,这篇论文提出 Regularized Softmax Deep Multi-Agent Q-Learning(RES-QMIX)。 首先,为了限制 Q 值不断变大,最直接的方法就是在损失函数加入一个正则化基线 b(s,u),使得 Q 值不会过多地偏离基线,论文将这种...