Deep Reinforcement Learning with Python
eBook Details:
- Paperback: 401 pages
- Publisher: WOW! eBook (April 16, 2021)
- Language: English
- ISBN-10: 1484268083
- ISBN-13: 978-1484268087
eBook Description:
Deep Reinforcement Learning with Python: With PyTorch, TensorFlow and OpenAI Gym: Explains deep reinforcement learning implementation using TensorFlow, PyTorch and OpenAI Gym
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 Deep Reinforcement Learning with Python 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.
What You’ll Learn
- Examine deep reinforcement learning
- Implement deep learning algorithms using OpenAI’s Gym environment
- Code your own game playing agents for Atari using actor-critic algorithms
- Apply best practices for model building and algorithm training
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 of the Deep Reinforcement Learning with Python: With PyTorch, TensorFlow and OpenAI Gym book, you’ll understand deep reinforcement learning along with deep q-networks and policy gradient models implementation with TensorFlow, PyTorch, and Open AI Gym.
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