Hands-On-Intelligent-Agents-with-OpenAI-Gym
Basic Information
This repository is the official code companion for the book Hands-On Intelligent Agents with OpenAI Gym. It provides hands-on example implementations to help readers get started with building deep reinforcement learning agents using the OpenAI Gym toolkit and the PyTorch library. The code is intended to illustrate core concepts from the book, demonstrate agent training and evaluation workflows in Gym environments, and serve as a practical resource for learning how to implement, run, and experiment with reinforcement learning algorithms. The repository focuses on practical, runnable code rather than theoretical exposition, and is designed to accompany the book's chapters and examples.
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Features
Contains example implementations of deep reinforcement learning agents that demonstrate how to interact with OpenAI Gym environments and train models using PyTorch. Provides sample code intended to mirror the hands-on exercises and examples presented in the book. Includes scripts and structured code to show common training and evaluation patterns for reinforcement learning. Emphasizes reproducible learning workflows so readers can run experiments and observe agent behaviour in standard Gym tasks. The repository is organized to support learners following the book, highlighting practical usage of Gym APIs and PyTorch model training loops.
Use Cases
This codebase helps learners and practitioners reproduce and explore the practical aspects of reinforcement learning from the associated book. It enables readers to run example agents, observe training dynamics, and gain familiarity with integrating Gym environments and PyTorch models. The repository is useful for students, instructors, and developers who want runnable examples to study algorithm implementations, experiment with hyperparameters, and extend agents for custom environments. By providing concrete code tied to the book"s chapters, it lowers the barrier to applying RL concepts in practice and accelerates hands-on learning of model training, evaluation, and iteration.
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