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reinforcement-learning

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Project Description

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

reinforcement-learning: Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and S

Project Title

reinforcement-learning — Comprehensive Implementation of Reinforcement Learning Algorithms

Overview

The reinforcement-learning project is a comprehensive repository of Reinforcement Learning (RL) algorithms implemented in Python, utilizing OpenAI Gym and TensorFlow. It serves as an educational tool, providing exercises and solutions that complement popular RL textbooks and courses, such as Sutton's book and David Silver's course.

Key Features

  • Implementation of various RL algorithms in Python
  • Exercises and solutions for popular RL textbooks and courses
  • Use of OpenAI Gym for RL environments
  • Advanced techniques using TensorFlow for neural network implementations

Use Cases

  • Educators and students learning Reinforcement Learning
  • Researchers and developers implementing RL algorithms in their projects
  • Practitioners looking for a hands-on approach to understanding RL concepts

Advantages

  • Provides a practical, hands-on approach to learning RL
  • Offers a wide range of implemented algorithms for reference and study
  • Supports advanced techniques with TensorFlow integration

Limitations / Considerations

  • Some sections and algorithms are marked as work-in-progress (WIP)
  • The project may require a solid understanding of Python and mathematical concepts in RL

Similar / Related Projects

  • fast.ai: A deep learning library in Python that is more focused on high-level APIs and less on individual algorithm implementations.
  • Deep RL Bootcamp: A collection of notebooks and resources for deep reinforcement learning, with a focus on state-of-the-art techniques.
  • Spinning Up in Deep RL: A comprehensive resource for learning deep RL, including implementations and tutorials, but with a focus on OpenAI's approach and tools.

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Project Information

Created on 8/24/2016
Updated on 11/2/2025