Artificial Inteligence
  • Preface
  • Introduction
  • Machine Learning
    • Linear Algebra
    • Supervised Learning
      • Neural Networks
      • Linear Classification
      • Loss Function
      • Model Optimization
      • Backpropagation
      • Feature Scaling
      • Model Initialization
      • Recurrent Neural Networks
        • Machine Translation Using RNN
    • Deep Learning
      • Convolution
      • Convolutional Neural Networks
      • Fully Connected Layer
      • Relu Layer
      • Dropout Layer
      • Convolution Layer
        • Making faster
      • Pooling Layer
      • Batch Norm layer
      • Model Solver
      • Object Localization and Detection
      • Single Shot Detectors
        • Yolo
        • SSD
      • Image Segmentation
      • GoogleNet
      • Residual Net
      • Deep Learning Libraries
    • Unsupervised Learning
      • Principal Component Analysis
      • Generative Models
    • Distributed Learning
    • Methodology for usage
      • Imbalanced/Missing Datasets
  • Artificial Intelligence
    • OpenAI Gym
    • Tree Search
    • Markov Decision process
    • Reinforcement Learning
      • Q_Learning_Simple
      • Deep Q Learning
      • Deep Reinforcement Learning
    • Natural Language Processing
      • Word2Vec
  • Appendix
    • Statistics and Probability
      • Probability
        • Markov Chains
        • Random Walk
    • Lua and Torch
    • Tensorflow
      • Multi Layer Perceptron MNIST
      • Convolution Neural Network MNIST
      • SkFlow
    • PyTorch
      • Transfer Learning
      • DataLoader and DataSets
      • Visualizing Results
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  1. Artificial Intelligence

Tree Search

PreviousOpenAI GymNextMarkov Decision process

Last updated 5 years ago

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Introduction

On this chapter we will learn about some ways to do tree search. Just to remember from the introduction tree search is one of the mechanisms to do planning. Planning means that the agent will simulate possible actions on a model of the word, and choose the one that will maximize it's utility.

On this chapter we will learn the following techniques to tree searching

  • Depth first search

  • Breadth-First search

  • Uniform Cost search

  • Greedy search

  • A-star search A*

As mentioned before we cannot hold the whole tree on memory, so what we do is to expand the tree only when you needed it and you keep track of the other options that you did not explored yet.

To those parts that are still on memory but not expanded yet we call fringe.

Depth first search