Deep Reinforcement Learning Hands-On is a code repository accompanying a published book that provides practical examples of deep reinforcement learning techniques.
The repository contains working code samples designed to teach deep reinforcement learning through hands-on implementation. The examples cover foundational concepts and progressively build toward more complex applications, with code organized by chapter. The project addresses the challenge of keeping educational material current as underlying libraries evolve, particularly PyTorch and gym, which are core dependencies for the examples.
This repository suits developers and students learning deep reinforcement learning who want to study working code alongside book material. It is most valuable for those building foundational understanding rather than those seeking a production framework. The examples demonstrate practical implementation patterns using PyTorch, making it useful for anyone planning to apply reinforcement learning techniques in their own projects.
The project maintains active compatibility with current versions of its dependencies, with the author regularly updating examples to work with the latest stable releases of PyTorch and gym. The repository uses tags and branches to track major code changes, allowing users to reference code as it existed at book publication or access updated versions. The author acknowledges that exact code may differ from the book text due to software evolution and that bugs in examples are inevitable, setting realistic expectations for users relying on this material.
