This review introduces the Mental World Model (MWM) as a new cognitive layer for Embodied AI, compares it with traditional Physical World Models, outlines 19 Theory‑of‑Mind methods, 26 evaluation benchmarks, and discusses key challenges and future research directions.
This article explains the fundamental architecture of reinforcement learning, contrasting model‑free and model‑based approaches, detailing environment models, planning, data augmentation, expert iteration, and embedding planning, and then examines how large language models use policy‑based methods such as PPO, DPO, and GRPO for RL‑HF.
This article explains the principles of collaborative filtering, outlines its three main types—user‑based, item‑based, and model‑based—and focuses on model‑based approaches such as matrix factorization, clustering, and deep‑learning techniques for recommending personalized game items to improve player experience and monetization.
