Hands-On-Reinforcement-Learning-with-Python  by PacktPublishing

RL learning resource using OpenAI Gym and TensorFlow

created 7 years ago
821 stars

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Project Summary

This repository provides code examples for the "Hands-On Reinforcement Learning with Python" book, targeting machine learning developers and AI enthusiasts. It aims to teach fundamental and advanced reinforcement learning (RL) and deep RL algorithms using OpenAI Gym and TensorFlow.

How It Works

The book's approach involves understanding core RL concepts like Markov Decision Processes and Bellman's optimality, then applying them to practical problems. It covers algorithms such as TD learning and multi-armed bandits, progressing to deep learning models like RNNs, LSTMs, and CNNs for RL applications. The code is structured by chapter, demonstrating agent training and algorithm implementation.

Quick Start & Requirements

  • Installation: Code is organized into folders by chapter (e.g., Chapter02).
  • Prerequisites: Anaconda, Chrome.
  • OS: Ubuntu or macOS.
  • Knowledge: Basic linear algebra, calculus, and Python are recommended.
  • Resources: A PDF with color images is available for download.

Highlighted Details

  • Covers basic RL methods and advanced deep RL algorithms.
  • Demonstrates training an agent to walk using OpenAI Gym and TensorFlow.
  • Explains Markov Decision Process, Bellman's optimality, and TD learning.
  • Includes applications of RNN, LSTM, and CNN in RL.

Maintenance & Community

The repository is associated with author Sudharsan Ravichandiran, a data scientist and AI enthusiast with a focus on RL and deep learning implementations.

Licensing & Compatibility

The repository's license is not specified in the README. Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

The README does not specify a license, which may impact commercial use or integration into proprietary projects. Specific version requirements for Anaconda or Python are not listed.

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Last commit

2 years ago

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