{"product_id":"reinforcement-learning-in-python-pytorch-a-practical-guide-to-modern-rl-algorithms-and-python-implementations-from-theory-to-deep-rl-real-world-9798290162072","title":"Reinforcement Learning in Python \u0026 PyTorch: A Practical Guide to Modern RL Algorithms and Python Implementations: From Theory to Deep RL \u0026 Real-World","description":"\u003cp\u003e • Author(s): Pythquill Publishing\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Programming Languages - Python\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eYou'll Learn\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eGrasp the Foundational Theory of Reinforcement Learning: \u003c\/b\u003e Understand the core components of RL, including the agent-environment interface, Markov Decision Processes (MDPs), and the Bellman equations that form the mathematical backbone of decision-making under uncertainty.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eMaster Classic RL Algorithms: \u003c\/b\u003e Learn and implement fundamental model-free and model-based algorithms like \u003cb\u003eMonte Carlo methods\u003c\/b\u003e, \u003cb\u003eTemporal Difference (TD) learning (SARSA and Q-Learning)\u003c\/b\u003e, and \u003cb\u003eDynamic Programming\u003c\/b\u003e to solve problems in simplified environments like Grid World and classic games.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eImplement Modern Deep Reinforcement Learning Algorithms: \u003c\/b\u003e Use deep neural networks as function approximators to scale RL to complex, high-dimensional problems. You will build and train state-of-the-art agents using \u003cb\u003eDeep Q-Networks (DQN)\u003c\/b\u003e, \u003cb\u003ePolicy Gradients (REINFORCE)\u003c\/b\u003e, and \u003cb\u003eActor-Critic methods (A2C\/A3C)\u003c\/b\u003e.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eTackle Continuous Control Tasks: \u003c\/b\u003e Learn advanced actor-critic algorithms like \u003cb\u003eDDPG, TD3, and SAC\u003c\/b\u003e to train agents for tasks with continuous action spaces, such as robotics control and other complex simulations.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eBuild and Debug Practical RL Systems in Python: \u003c\/b\u003e Gain hands-on experience by implementing algorithms from scratch using popular libraries like \u003cb\u003eNumPy, PyTorch, and Gymnasium\u003c\/b\u003e. You will learn essential debugging strategies, hyperparameter tuning techniques, and best practices for evaluating your agents' performance.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eExplore Advanced and Cutting-Edge Topics: \u003c\/b\u003e Dive into specialized areas of RL, including \u003cb\u003eMulti-Agent Systems (MARL)\u003c\/b\u003e, \u003cb\u003eHierarchical Reinforcement Learning (HRL)\u003c\/b\u003e, \u003cb\u003eModel-Based RL\u003c\/b\u003e, and \u003cb\u003eOffline RL\u003c\/b\u003e. You will also learn about the revolutionary concept of \u003cb\u003eReinforcement Learning from Human Feedback (RLHF)\u003c\/b\u003e and its role in aligning large language models.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eApply RL to Real-World Case Studies: \u003c\/b\u003e Understand how to frame diverse real-world problems-from robotics and game-playing to recommender systems and resource management-as RL problems and select the appropriate algorithms to solve them.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eAddress the Challenges and Ethics of RL: \u003c\/b\u003e Recognize key challenges like the exploration-exploitation dilemma and the \"deadly triad.\" You will also gain an understanding of the ethical considerations, safety, and societal impact of deploying RL systems.\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47577003131031,"sku":"9798290162072","price":1986.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798290162072.webp?v=1774902301","url":"https:\/\/atlanticbooks.com\/products\/reinforcement-learning-in-python-pytorch-a-practical-guide-to-modern-rl-algorithms-and-python-implementations-from-theory-to-deep-rl-real-world-9798290162072","provider":"Atlantic Books","version":"1.0","type":"link"}