Nash Q-Learning算法是将Minimax-Q算法从零和博弈扩展到多人一般和博弈的算法。在Minimax-Q算法中需要通过Minimax线性规划求解阶段博弈的纳什均衡点,拓展到Nash Q-Learning算法就是使用二次规划求解纳什均衡点,具体求解方法后面单独开一章讲解。Nash Q-Learning算法在合作性均衡或对抗性均衡的环境中能够收敛到纳什均衡点...
Nash Q-Learning算法 是将Minimax-Q算法扩展到多人一般和博弈场景。它使用二次规划求解纳什均衡点，适用于合作或对抗环境。然而，算法的收敛性依赖于每个状态的阶段博弈中存在全局最优点或鞍点，这在实际应用中可能不易满足。Friend-or-Foe Q-Learning算法（FFQ） 则是Minimax-Q算法的进一步拓展，旨在处理...
Minimax Q-learningPolicy iterationThe H;control method is an effective approach for attenuating the effect of disturbances on practical systems, but it is difficult to obtain the H;controller due to the nonlinear Hamilton-Jacobi-Isaacs equation, even for linear systems. This study deals with the ...
1A Two-Step Minimax Q-learning Algorithm forTwo-Player Zero-Sum Markov GamesShreyas S R ∗ , Antony Vijesh†Abstract—An interesting iterative procedure is proposed tosolve a two-player zero-sum Markov games. First this problemis expressed as a min-max Markov game. Next, a two step Q-l...
为了简化这个过程，Friend-or-Foe Q-Learning应运而生，它巧妙地将一般博弈转化为零和形式，使得每个智能体可以独立学习，但行动更新仍然依赖于对手的策略。FFQ和Minimax-Q都需要较大的空间存储，而WoLF-PHC则带来了突破，它通过Win or Learn Fast（快速获胜或学习）策略和policy hill-climbing（策略爬坡）...
Minimax-Optimal Multi-Agent Robust Reinforcement Learning Multi-agent robust reinforcement learning, also known as multi-player robust Markov games (RMGs), is a crucial framework for modeling competitive interacti... Y Jiao,G Li 被引量: 0发表: 2024年 A Multi-Step Minimax Q-learning Algorithm ...
如何将MinMax树与Q-Learning结合使用? 浏览2提问于2012-01-10得票数3 回答已采纳 2回答 AI象棋有效走法 、、 我正在尝试编写AI国际象棋,但我有一个问题。我已经准备好了棋子的移动规则,我正在尝试删除无效的移动(将国王留在检查中等)。我写了这样的东西:{if(board[i]==king.opposite) kingpos=board[i]; ...
然后,引入神经网络来近似求解大规模问题的Q函数。提出了一种在线极小极大Q网络学习算法,利用观测数据对网络进行训练。采用经验重放(Experience Replay)、对抗网路(dueling network)、双Q学习(Double Q-learning)等方法改进学习过程。 ——— 版权声明:本文为CSDN博主「码丽莲梦露」的原创文章,遵循CC 4.0 BY-SA版权协...
As mentioned above, the Minimax Q learning paper gives a different formula for the bellman equation at the bottom left of page 3. Now that we have transformed the problem back into the framework of 1 network controlling agents in an environment, we can use all the techniques of Deep Q Lea...
Deep Q-Learning (DQN) Minimax Algorithm Dynamic Rewards for RL Training Each implementation provides insights into the training process and strategies for decision-making in ConnectX. Explore, experiment, and enhance these models to improve their performance in ConnectX!About...