{"id":8335,"date":"2026-08-10T09:54:47","date_gmt":"2026-08-10T09:54:47","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/oreilly-deep-reinforcement-learning-in-action-video-edition-2020-3\/"},"modified":"2026-08-10T09:54:47","modified_gmt":"2026-08-10T09:54:47","slug":"oreilly-deep-reinforcement-learning-in-action-video-edition-2020-3","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/oreilly-deep-reinforcement-learning-in-action-video-edition-2020-3\/","title":{"rendered":"Oreilly \u2013 Deep Reinforcement Learning in Action, Video Edition 2020-3"},"content":{"rendered":"<div class=\"w-post-elm post_content\">\n<h2 style=\"text-align: left\"><span style=\"vertical-align: inherit\">Description<\/span><\/h2>\n<div class=\"presented-response-container ng-tns-c357060534-66\" style=\"text-align: left\">\n<div class=\"response-container-content ng-tns-c357060534-66\">\n<div class=\"response-content ng-tns-c357060534-66\">\n<div class=\"markdown markdown-main-panel\">\n<p data-sourcepos=\"7:1-7:312\"><span style=\"vertical-align: inherit\">Deep Reinforcement Learning in Action, Video Edition. This course teaches you how to use deep reinforcement learning to build AI agents that can learn and improve based on feedback from their environment. This technique allows algorithms to learn new skills through trial and error, just like humans. Key points: Learning through experience and feedback, Solving complex problems using AI, Building human-like AI agents, Using popular tools like PyTorch and OpenAI Gym.<\/span><\/p>\n<h3 data-sourcepos=\"9:1-9:26\"><span style=\"vertical-align: inherit\">What you will learn:<\/span><\/h3>\n<ul data-sourcepos=\"10:1-14:0\">\n<li data-sourcepos=\"10:1-10:98\"><span style=\"vertical-align: inherit\">Building and Training DRL Networks: Learn how to build and train neural networks for deep reinforcement learning.<\/span><\/li>\n<li data-sourcepos=\"11:1-11:106\"><span style=\"vertical-align: inherit\">Popular DRL Algorithms: Introduction to the most widely used deep reinforcement learning algorithms for solving various problems.<\/span><\/li>\n<li data-sourcepos=\"12:1-12:100\"><span style=\"vertical-align: inherit\">Evolutionary Algorithms: Learn about evolutionary algorithms to foster curiosity and multi-agent learning.<\/span><\/li>\n<li data-sourcepos=\"13:1-14:0\"><span style=\"vertical-align: inherit\">Practical Implementation: All examples are presented as Jupyter notebooks so you can easily run them.<\/span><\/li>\n<\/ul>\n<h3 data-sourcepos=\"15:1-15:29\"><span style=\"vertical-align: inherit\">Who is it suitable for?<\/span><\/h3>\n<ul>\n<li data-sourcepos=\"16:1-16:101\"><span style=\"vertical-align: inherit\">This course is suitable for people with intermediate knowledge of the Python programming language and deep learning.<\/span><\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<h3 style=\"text-align: left\"><span style=\"vertical-align: inherit\">Course details: Deep Reinforcement Learning in Action, Video Edition<\/span><\/h3>\n<ul style=\"text-align: left\">\n<li><span style=\"vertical-align: inherit\">Publisher: <\/span><a href=\"https:\/\/href.li\/?https:\/\/www.oreilly.com\/library\/view\/deep-reinforcement-learning\/9781617295430VE\/?_gl=1*1gufiz9*_ga*MTU4NjE0OTc3OC4xNzEyMjIyNDU3*_ga_092EL089CH*MTczODEyODY3NC4zNC4xLjE3MzgxMzA2OTIuNDcuMC4w\" target=\"_blank\" rel=\"noopener\"><span style=\"vertical-align: inherit\">Oreilly<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Instructor: <\/span><a class=\"author-name\" href=\"https:\/\/downloadlynet.ir\/tag\/brandon-brown\/\"><span style=\"vertical-align: inherit\">Brandon Brown<\/span><\/a><span style=\"vertical-align: inherit\"> ,&nbsp; <\/span><a class=\"author-name\" href=\"https:\/\/downloadlynet.ir\/tag\/alexander-zai\/\"><span style=\"vertical-align: inherit\">Alexander Zai<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Training level: Beginner to advanced<\/span><\/li>\n<li><span style=\"vertical-align: inherit\">Training duration: 12 hours<\/span><\/li>\n<\/ul>\n<h3 style=\"text-align: left\"><span style=\"vertical-align: inherit\">Course topics<\/span><\/h3>\n<div class=\"ud-block-list-item ud-block-list-item-small ud-block-list-item-tight ud-block-list-item-neutral ud-text-sm\">\n<ul style=\"text-align: left\">\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 1. Foundations<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. What is reinforcement learning?<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Reinforcement learning<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Dynamic programming versus Monte Carlo<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. The reinforcement learning framework<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. What can I do with reinforcement learning?<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Why deep reinforcement learning?<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Our didactic tool: String diagrams<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. What\u2019s next?<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Summary<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Modeling reinforcement learning problems: Markov decision processes<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Solving the multi-arm bandit<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Applying bandits to optimize ad placements<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Building networks with PyTorch<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Solving contextual bandits<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. The Markov property<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Predicting future rewards: Value and policy functions<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Summary<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Predicting the best states and actions: Deep Q-networks<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Navigating with Q-learning<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Preventing catastrophic forgetting: Experience replay<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Improving stability with a target network<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Review<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Summary<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Learning to pick the best policy: Policy gradient methods<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Reinforcing good actions: The policy gradient algorithm<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Working with OpenAI Gym<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. The REINFORCE algorithm<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Summary<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Tackling more complex problems with actor-critic methods<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Distributed training<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Advantage actor-critic<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. N-step actor-critic<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Summary<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 2. Above and beyond<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Alternative optimization methods: Evolutionary algorithms<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Reinforcement learning with evolution strategies<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. A genetic algorithm for CartPole<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Pros and cons of evolutionary algorithms<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Evolutionary algorithms as a scalable alternative<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Summary<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Distributional DQN: Getting the full story<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Probability and statistics revisited<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. The Bellman equation<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Distributional Q-learning<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Comparing probability distributions<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Dist-DQN on simulated data<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Using distributional Q-learning to play Freeway<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Summary<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Curiosity-driven exploration<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Inverse dynamics prediction<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Setting up Super Mario Bros.<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Preprocessing and the Q-network<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Setting up the Q-network and policy function<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Intrinsic curiosity module<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Alternative intrinsic reward mechanisms<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Summary<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Multi-agent reinforcement learning<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Neighborhood Q-learning<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. The 1D Ising model<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Mean field Q-learning and the 2D Ising model<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Mixed cooperative-competitive games<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Summary<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Interpretable reinforcement learning: Attention and relational models<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Relational reasoning with attention<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Implementing self-attention for MNIST<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Multi-head attention and relational DQN<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Double Q-learning<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Training and attention visualization<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Summary<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. In conclusion: A review and roadmap<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. The uncharted topics in deep reinforcement learning<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. The end<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix. Mathematics, deep learning, PyTorch<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix. Calculus<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix. Deep learning<\/span><\/li>\n<li style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix. PyTorch<\/span><\/li>\n<\/ul>\n<h3 style=\"text-align: left\"><span style=\"vertical-align: inherit\">Course images<\/span><\/h3>\n<p style=\"text-align: left\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-960015 size-full\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2025\/01\/Deep-Reinforcement-Learning-in-Action-Video-Edition-1.png\" alt=\"Deep Reinforcement Learning in Action, Video Edition\" width=\"1479\" height=\"441\"><\/p>\n<h3 style=\"text-align: left\"><span style=\"vertical-align: inherit\">Sample course video<\/span><\/h3>\n<div style=\"width: 640px;\" class=\"wp-video\"><span class=\"mejs-offscreen\">Video Player<\/span><\/p>\n<div id=\"mep_0\" class=\"mejs-container mejs-container-keyboard-inactive wp-video-shortcode mejs-video\" tabindex=\"0\" role=\"application\" aria-label=\"Video Player\" style=\"width: 640px; height: 360px; min-width: 217px;\">\n<div class=\"mejs-inner\">\n<div class=\"mejs-mediaelement\"><mediaelementwrapper id=\"video-153002-1\"><video class=\"wp-video-shortcode\" id=\"video-153002-1_html5\" width=\"640\" height=\"360\" preload=\"metadata\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Deep_Reinforcement_Learning_in_Action_Video_Edition_Downloadly.ir.mp4?_=1\" style=\"width: 640px; 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This course teaches you how to use deep reinforcement learning to build AI agents that can lea<\/p>\n","protected":false},"author":1,"template":"","dgi_category":[10458],"dgi_tag":[73972,18317,73973,73974,73975,73976,73977],"class_list":["post-8335","digital_item","type-digital_item","status-publish","has-post-thumbnail","hentry","dgi_category-video-tutorials","dgi_tag-alexander-zai","dgi_tag-brandon-brown","dgi_tag-course-deep-reinforcement-learning-in-action-video-edition","dgi_tag-download-course-deep-reinforcement-learning-in-action-video-edition","dgi_tag-download-deep-reinforcement-learning-in-action-video-edition","dgi_tag-free-deep-reinforcement-learning-in-action-video-edition","dgi_tag-free-download-deep-reinforcement-learning-in-action-video-edition"],"_links":{"self":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/8335","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item"}],"about":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/types\/digital_item"}],"author":[{"embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"version-history":[{"count":0,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/8335\/revisions"}],"wp:attachment":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/media?parent=8335"}],"wp:term":[{"taxonomy":"dgi_category","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_category?post=8335"},{"taxonomy":"dgi_tag","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_tag?post=8335"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}