Covers the latest deep reinforcement learning algorithms for up-to-date knowledge
Includes hands-on coding examples using Jupyter notebooks for practical learning
Explains multi-agent reinforcement learning and human feedback training for advanced applications
Walks through deploying models on platforms like Hugging Face Hub for real-world deployment
Explores use cases in gaming, robotics, and finance to showcase diverse RL applications
Summarized by Shop
Gain a theoretical understanding to the most popular libraries in deep reinforcement learning (deep RL). This new edition focuses on the latest advances in deep RL using a learn-by-coding approach, allowing readers to assimilate and replicate the latest research in this field. New agent environments ranging from games, and robotics to f
Format
E-Book
Language
English
Target Audience
Adults
Libraries Covered
StableBaselines3, CleanRL, Gymnasium, PyBullet, Unity ML
Deployment
Jupyter Notebook compatible with Google Colab and similar platforms
Advanced Topics
Multi-agent RL, Proximal Policy Optimization (PPO), RLHF with large language models