reinforcement learning – Buonaiuto@Work

Markov Chains, MDPs, and Memory-Augmented MDPs: The Mathematical Core of Agentic AI

Massimo2025-09-010148 views

Markov Chains, Markov Decision Processes (MDP), and Memory-augmented MDPs (M-MDP) form the mathematical backbone of decision-making under uncertainty. While Markov Chains capture stochastic dynamics, MDPs extend them with actions and rewards. Yet, real-world tasks demand memory—this is where M-MDPs shine. By embedding structured memory into the agent’s state, M-MDPs enable agentic AI systems to reason, plan, and adapt across long horizons. This blog post explores the mathematics, technicalities, and the disruptive role of M-MDPs in modern AI architectures.

A gentle introduction to Graph Neural Network (GNN)

Massimo2022-12-2104,1K views

Graph Neural Networks (GNNs) revolutionize data processing by leveraging graph structures, enabling advanced applications from social network analysis to molecular studies. Central to GNNs is the message-passing mechanism, which facilitates node communication, enhancing data representation based on neighboring relationships. This process iteratively updates node states, capturing intricate patterns within graph data, thus offering superior insights for tasks like classification and prediction. GNNs’ ability to incorporate graph topology into learning models marks a significant advancement in machine learning, addressing complex problems across various domains with unprecedented accuracy and efficiency.

Learning World Models Better Than The World Itself

Massimo2022-09-0504,K views

The blog post delves into the concept of learning world models more effectively than reality itself, focusing on Denoised MDPs (Markov Decision Processes). By filtering out irrelevant information, these models enhance an agent’s decision-making capabilities. This innovative approach, elucidated by Wang et al., demonstrates how artificial agents can discern and utilize only pertinent data for optimal performance in various tasks. Through rigorous experimentation and theoretical groundwork, the study showcases the superiority of denoised world models over conventional methods. Explore more about Denoised MDPs and their implications in navigating complex environments.