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AI Strategy Optimization Using Monte Carlo Tree Search - From Theory to Implementation

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Overview

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Learn how AI agents leverage Monte Carlo Tree Search (MCTS) algorithms to calculate and select optimal future outcomes in this 26-minute educational video. Dive into the technical differences between Monte Carlo Particle Filters for dynamic system estimation and MCTS for decision-making simulation. Explore practical applications in healthcare, where MCTS helps create personalized treatment plans for chronic conditions by analyzing patient data, medical history, and real-time health monitoring information. Follow along with hands-on demonstrations using Python code and a free Google Colab notebook to understand the implementation of MCTS in AI systems. Master the strategy update process through self-play, understand the step-by-step MCTS explanation, and discover how to incorporate ethics filters into simulations. Examine real-world applications in medical and financial forecasting, while gaining insights into the "Tomorrow Machine" concept for optimizing future outcomes. Access provided resources include the original research paper on STRATEGIST, complete Python code on GitHub, and an interactive Colab notebook for practical experimentation.

Syllabus

Let us find the best future
Monte Carlo Tree Search - MCTS in AI Agents
Strategist: Learning Strategic Skills
Strategy update process with Self-play and MCTS
MCTS explained step by step
Add an ethics filter to MCTS simulations
Google COLAB NB Strategist w/ MCTS
MCTS Python Code
My Example of Strategist Framework
Forecasting Med, Finance by AI Agents

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