博弈论与强化学习 算法 一 MinimaxQ, NashQ ,FFQ
2.1 引言 一个随机博弈可以看成是一个多智能体强化学习过程,但其实这两个概念不能完全等价，随机博弈中假定每个状态的奖励矩阵是已知的，不需要学习。而多智能体强化学习则是通过与环境的不断交互来学习每个状态的奖励值函数，再通过这些奖励值函数来学习得到最优纳什策略。通常情况下，模型的转移概率以及奖励函数为止，因此需要利用到Q-learning中的方法来不断逼近状态值函数或动作-状态值函数。 在多智能体强化学习算法中，两个主要的技术指标为合理性与收敛性。 合理性（rationality）是指在对手使用一个恒定策略的情况下，当前智能体能够学习并收敛到一个相对于对手策略的最优策略。 收敛性（convergence）是指在其他智能体也使用学习算法时，当前智能体能够学习并收敛到一个稳定的策略。通常情况下，收敛性针对系统中的所有的智能体使用相同的学习算法。 针对应用来分，多智能体强化学习算法可分为零和博弈算法与一般和博弈算法。 本文主要介绍四种多智能体强化学习算法，主要介绍每种算法的应用特性与应用公式，具体的收敛性证明后面每个算法单开一章讲。 2.2 Minimax-Q算法 相关论文 Littman, Michael L. “Markov games as a framework for multi-agent reinforcement learning.” Machine learning proceedings 1994. Morgan Kaufmann, 1994. 157-163. cited: 2970 Machine Learning Proceedings 1994 : Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, July 10–13, 1994 Michael Littman Michael […]
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Latest 23 mins 24.11.2021 A Caesar’s Guide to Casino Games Casino games vary in style, payouts, strategy, and more. There are handfuls of games available as well as multitudes of versions of each! In this guide, we will cover the four main non-slot casino games and their variations along with two more games you may […]
Azure の更新情報 | Microsoft Azure
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Double Deep Q-Networks (DDQN) – A Quick Intro (with Code)
In the previous post, we discussed how Deep Q-Networks has proven to be a powerful tool for solving RL-based problems. However, over the years, several modifications to it have resulted in a great performance. In this article, I’ll discuss one of these modifications known as Double Deep Q-Networks (DDQN). Overestimation in Q-learning One of the […]
Azure Virtual Machines | Microsoft Azure
Each Azure virtual machine has a certain allocation of hardware, including CPU cores, memory, hard drives, network interfaces, and other devices to run a wide range of operating systems, applications, and workloads in the Azure cloud environment. These hardware resources are partitioned within an Azure datacenter to create Azure virtual machines. Source
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Understanding when Dynamics-Invariant Data Augmentations Benefit Model-Free Reinforcement Learning Updates
[Submitted on 26 Oct 2023 (v1), last revised 16 Mar 2024 (this version, v2)] View a PDF of the paper titled Understanding when Dynamics-Invariant Data Augmentations Benefit Model-Free Reinforcement Learning Updates, by Nicholas E. Corrado and 1 other authors View PDF HTML (experimental) Abstract:Recently, data augmentation (DA) has emerged as a method for leveraging domain […]
AI learns how vision and sound are connected, without human intervention
Humans naturally learn by making connections between sight and sound. For instance, we can watch someone playing the cello and recognize that the cellist’s movements are generating the music we hear. A new approach developed by researchers from MIT and elsewhere improves an AI model’s ability to learn in this same fashion. This could be […]
Weights and Bias in Neural Networks
Machine learning, with its ever-expanding applications in various domains, has revolutionized the way we approach complex problems and make data-driven decisions. At the heart of this transformative technology lies neural networks, computational models inspired by the human brain’s architecture. Neural networks have the remarkable ability to learn from data and uncover intricate patterns, making them […]