reinforcement learning

The Cognitive Familiarity Supremacy Theory (CFST)

The Cognitive Familiarity Supremacy Theory (CFST) proposes that a substantial portion of human certainty, ideological attachment, collective identity formation, and perceived superiority emerges not primarily from objective rational evaluation, but from repeated familiarity encoding mechanisms operating within subconsc...

DIArc Foundational Note v0.1 — Minimum Claim Edition

Abstract The rapid development of artificial intelligence has significantly increased the availability of information, analytical capability, and machine-assisted reasoning. However, greater access to information does not necessarily produce better decisions. In many organizational contexts, the emerging bottleneck is...

Learning by Consequence: A Narrative Review of Reinforcement Learning from Thorndike's Law of Effect to Deep Q-Networks and AlphaGo

Reinforcement learning---learning what to do from reward and punishment rather than from instruction---unifies animal psychology, optimal control, and machine learning into one computational program, and its deep-learning era delivered the field's most visible artificial intelligence achievements. This article presents...

Student Behavior Recognition and Intervention Methods in Intelligent Classrooms Based on Deep Reinforcement Learning

In response to the problem that traditional classroom student behavior analysis relies on manual observation and is difficult to achieve real-time, precise and personalized intervention. This paper constructs an end-to-end intelligent classroom intervention system based on deep reinforcement learning. This system adopt...

Distribution Estimation Algorithm for Cloud Manufacturing Scheduling Optimization

To address the resource scheduling problem in complex production environments, this study proposes a production scheduling model based on Estimation of Distribution Algorithm.The model constructs a probability model using spatial distribution, evaluates the scheduling population based on high-quality individuals, intro...

Learning by Watching: A Narrative Review of Imitation Learning from ALVINN to Generative Adversarial Imitation

Imitation learning---the learning of behavior from demonstrations instead of rewards---moved from Pomerleau's ALVINN driving network and Schaal's humanoid route through Ng and Russell's inverse reinforcement learning, Abbeel and Ng's apprenticeship learning, and Ziebart's maximum entropy to the robot learning from demo...

PBFT-CG-MARL

PBFT-CG-MAPPO is a consensus-conditioned multi-agent reinforcement learning framework that embeds a Practical Byzantine Fault Tolerance (PBFT) three-phase commit protocol inside the CTDE-MAPPO training loop as an additive consensus loss with zero initialization, complemented by an entropy floor that prevents premature...

Learning Among Learners: A Narrative Review of Multi-Agent Reinforcement Learning from Markov Games to Deep Emergent Play

Multi-agent reinforcement learning---the learning of behavior when the environment's other agents learn too---moved from Tan's independent learners and Littman's Markov games framework through the cooperative dynamics' analyses and the surveys' question to the deep era's communication, actor-critics, value decompositio...

Integrasi Teori Perilaku Belajar dan Rekonstruksi Penguatan dalam Pembelajaran Pendidikan Agama Islam

In educational psychology, the theory of student learning behavior is one of the most important topics. The diversity of theories on this subject demonstrates the numerous ways teachers can easily understand students’ conditions and master how to teach the lessons they will teach. The purpose of this study is to focus...

Smartpath-Rpl: A Deep Reinforcement Learning-Based Optimized Routing Protocol For Mobile Low-Power And Lossy Networks

PBFT-CG-MARL

PBFT-CG-MAPPO is a consensus-conditioned multi-agent reinforcement learning framework that embeds a Practical Byzantine Fault Tolerance (PBFT) three-phase commit protocol inside the CTDE-MAPPO training loop as an additive consensus loss with zero initialization, complemented by an entropy floor that prevents premature...

Learning by Watching: A Narrative Review of Imitation Learning from ALVINN to Generative Adversarial Imitation

Imitation learning---the learning of behavior from demonstrations instead of rewards---moved from Pomerleau's ALVINN driving network and Schaal's humanoid route through Ng and Russell's inverse reinforcement learning, Abbeel and Ng's apprenticeship learning, and Ziebart's maximum entropy to the robot learning from demo...