Deep Reinforcement Learning in Action | AIBH New Arrival

Product Full Description

Deep Reinforcement Learning in Action is a comprehensive guide that bridges the gap between theoretical concepts and practical implementation of deep reinforcement learning (DRL). Authored by Alexander Zai and Brandon Brown, this book is tailored for readers with intermediate skills in Python and deep learning, aiming to equip them with the knowledge and tools to develop AI agents capable of learning from direct interaction with their environment.

The book delves into the fundamentals of DRL, covering essential topics such as Markov decision processes, deep Q-networks, policy gradient methods, and actor-critic methods. It further explores advanced subjects including evolutionary algorithms, distributional DQNs, curiosity-driven exploration, multi-agent reinforcement learning, and interpretable reinforcement learning through attention and relational models.

Utilizing industry-standard tools like PyTorch and OpenAI Gym, the authors provide hands-on examples and Jupyter Notebooks to facilitate experiential learning. Readers will engage in practical challenges such as navigating mazes and playing video games, reinforcing the application of DRL techniques in real-world scenarios.

This resource is invaluable for professionals and enthusiasts seeking to deepen their understanding of DRL and apply it effectively in various domains.

✅ Top 6 Key Features:

1. Comprehensive Coverage:
   Explores both foundational and advanced DRL techniques, providing a holistic understanding of the field.

2. Practical Implementation:
   Includes hands-on examples and Jupyter Notebooks using PyTorch and OpenAI Gym to reinforce learning.

3. Real-World Applications:
   Engages readers with practical challenges like maze navigation and video game playing to apply DRL concepts.

4. Advanced Topics:
   Delves into evolutionary algorithms, curiosity-driven exploration, and multi-agent reinforcement learning.

5. Interpretable Models:
   Introduces attention and relational models to enhance the interpretability of DRL systems.

6. Expert Authorship:
   Authored by professionals with extensive experience in machine learning and AI, ensuring authoritative content.