Balancing Information Value and Platform Survival in Underwater UUV C2 Decision Making

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.

Autonomous SystemsC2Decision Theory

0 likes · 14 min read

Balancing Information Value and Platform Survival in Underwater UUV C2 Decision Making

Data Party THU

Sep 15, 2025 · Artificial Intelligence

Agentic RL: Transforming LLMs into Autonomous Decision‑Making Agents

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.

AI AgentsLLMPOMDP

0 likes · 12 min read

Agentic RL: Transforming LLMs into Autonomous Decision‑Making Agents

Bighead's Algorithm Notes

Sep 14, 2025 · Artificial Intelligence

How MM‑DREX Uses Multimodal LLMs for Dynamic Expert Routing in Financial Trading

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.

Dynamic RoutingLLMPOMDP

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How MM‑DREX Uses Multimodal LLMs for Dynamic Expert Routing in Financial Trading

Code DAO

Apr 28, 2022 · Artificial Intelligence

Model-Based Reinforcement Learning from Raw Video: A Detailed Walkthrough

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.

CNN autoencoderPOMDPiLQR

0 likes · 11 min read

Model-Based Reinforcement Learning from Raw Video: A Detailed Walkthrough

DataFunTalk

Feb 27, 2020 · Artificial Intelligence

Technical Challenges in Planning and Control for Autonomous Heavy Trucks

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.

Autonomous DrivingPOMDPPlanning

0 likes · 13 min read

Technical Challenges in Planning and Control for Autonomous Heavy Trucks