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Deep reinforcement learning with Python : with Pytorch, Tensorflow and OpenAI Gym

Deep reinforcement learning with Python : with Pytorch, Tensorflow and OpenAI Gym / Nimish Sanghi.
[S.l.] : Apress, 2021.
Physical Description
1 online resource
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Access is available to the Yale community.
Includes index.
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Deep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics, healthcare, finance, and many more. This book covers deep reinforcement learning using deep-q learning and policy gradient models with coding exercise. You'll begin by reviewing the Markov decision processes, Bellman equations, and dynamic programming that form the core concepts and foundation of deep reinforcement learning. Next, you'll study model-free learning followed by function approximation using neural networks and deep learning. This is followed by various deep reinforcement learning algorithms such as deep q-networks, various flavors of actor-critic methods, and other policy-based methods. You'll also look at exploration vs exploitation dilemma, a key consideration in reinforcement learning algorithms, along with Monte Carlo tree search (MCTS), which played a key role in the success of AlphaGo. The final chapters conclude with deep reinforcement learning implementation using popular deep learning frameworks such as TensorFlow and PyTorch. In the end, you'll understand deep reinforcement learning along with deep q networks and policy gradient models implementation with TensorFlow, PyTorch, and Open AI Gym. You will: Examine deep reinforcement learning Implement deep learning algorithms using OpenAIs Gym environment Code your own game playing agents for Atari using actor-critic algorithms Apply best practices for model building and algorithm training.
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O'Reilly Safari. OCLC KB.
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Added to Catalog
June 01, 2021
Chapter 1: Introduction to Deep Reinforcement Learning
Chapter 2: Markov Decision Processes
Chapter 3: Model Based Algorithms
Chapter 4: Model Free Approaches
Chapter 5: Function Approximation
Chapter 6:Deep Q-Learning
Chapter 7: Policy Gradient Algorithms
Chapter 8: Combining Policy Gradients and Q-Learning
Chapter 9: Integrated Learning and Planning
Chapter 10: Further Exploration and Next Steps.
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