This article surveys the emerging field of Agentic Environment Engineering, defining environments as POMDPs, classifying their attributes and tasks, reviewing synthesis methods, evaluation frameworks, and outlining four complementary paths for agent evolution and three paradigms for environment evolution.
The article presents a comprehensive C2 decision framework for underwater UUVs, defining core variables, rule‑based and game‑theoretic models, POMDP and Monte‑Carlo solutions, risk‑aware algorithms, multi‑UUV consensus, and practical three‑layer rule implementations to balance information gain against platform survivability.
This survey formalizes the shift from preference‑based reinforcement fine‑tuning to Agentic Reinforcement Learning, defines Agentic RL via MDP/POMDP abstractions, proposes a dual taxonomy of capabilities and task domains, compiles over 500 recent works, and outlines open challenges for scalable, robust AI agents.
The article reviews the MM‑DREX framework, which tackles the non‑stationarity of financial markets by modeling trading as a POMDP, employing a vision‑language model‑driven dynamic router to allocate four heterogeneous experts, and demonstrates superior returns, Sharpe ratios, and drawdown control across stocks, futures, and crypto compared with 15 strong baselines.
The article explains how to train robots to learn tasks directly from raw video using model-based reinforcement learning, covering POMDP formulation, CNN auto‑encoders, latent‑space representations, iLQR optimization, and a step‑by‑step pipeline with concrete examples and references.
The article reviews the complex system model of autonomous heavy trucks, outlines traditional and modern planning and control methods—including rule‑based FSM, POMDP, learning‑based and optimization techniques—highlights safety, efficiency, fuel‑economy, and dynamic modeling challenges specific to heavy‑truck and trailer configurations, and shares practical attempts such as lane‑changing, merging, and trailer‑aware trajectory planning.
