{"product_id":"b097825gg3","title":"Deep Reinforcement Learning in Action","description":"\u003cp\u003eSummary\u003c\/p\u003e\n\n\u003cp\u003eHumans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot.  Deep Reinforcement Learning in Action  teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects.\u003c\/p\u003e\n\n\u003cp\u003eAbout the technology\u003c\/p\u003e\n\n\u003cp\u003eDeep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error.\u003c\/p\u003e\n\n\u003cp\u003eAbout the book\u003c\/p\u003e\n\n\u003cp\u003eDeep Reinforcement Learning in Action  teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym.\u003c\/p\u003e\n\n\u003cp\u003eWhat's inside\u003c\/p\u003e\n\n\u003cp\u003eBuilding and training DRL networks\u003cbr\u003e\nThe most popular DRL algorithms for learning and problem solving\u003cbr\u003e\nEvolutionary algorithms for curiosity and multi-agent learning\u003cbr\u003e\nAll examples available as Jupyter Notebooks\u003c\/p\u003e\n\n\u003cp\u003eAbout the reader\u003c\/p\u003e\n\n\u003cp\u003eFor readers with intermediate skills in Python and deep learning.\u003c\/p\u003e\n\n\u003cp\u003eAbout the author\u003c\/p\u003e\n\n\u003cp\u003eAlexander Zai  is a machine learning engineer at Amazon AI.  Brandon Brown  is a machine learning and data analysis blogger.\u003c\/p\u003e\n\n\u003cp\u003eTable of Contents\u003c\/p\u003e\n\n\u003cp\u003ePART 1 - FOUNDATIONS\u003c\/p\u003e\n\n\u003cp\u003e1. What is reinforcement learning?\u003c\/p\u003e\n\n\u003cp\u003e2. Modeling reinforcement learning problems: Markov decision processes\u003c\/p\u003e\n\n\u003cp\u003e3. Predicting the best states and actions: Deep Q-networks\u003c\/p\u003e\n\n\u003cp\u003e4. Learning to pick the best policy: Policy gradient methods\u003c\/p\u003e\n\n\u003cp\u003e5. Tackling more complex problems with actor-critic methods\u003c\/p\u003e\n\n\u003cp\u003ePART 2 - ABOVE AND BEYOND\u003c\/p\u003e\n\n\u003cp\u003e6. Alternative optimization methods: Evolutionary algorithms\u003c\/p\u003e\n\n\u003cp\u003e7. Distributional DQN: Getting the full story\u003c\/p\u003e\n\n\u003cp\u003e8.Curiosity-driven exploration\u003c\/p\u003e\n\n\u003cp\u003e9. Multi-agent reinforcement learning\u003c\/p\u003e\n\n\u003cp\u003e10. Interpretable reinforcement learning: Attention and relational models\u003c\/p\u003e\n\n\u003cp\u003e11. In conclusion: A review and roadmap\u003c\/p\u003e","brand":"ANT","offers":[{"title":"Ebook","offer_id":52836779032792,"sku":"YSL63493","price":29.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0835\/3356\/2072\/files\/71Pi1SFkyfL._SY1500.jpg?v=1793274478","url":"https:\/\/www.bookmebay.com\/products\/b097825gg3","provider":"BookMebay","version":"1.0","type":"link"}