- eBook:Deep Reinforcement Learning with Python: With PyTorch, TensorFlow and OpenAI Gym
- Author:Nimish Sanghi
- Edition:1 edition
- Data:May 16, 2021
- Pages:401 pages
- Format:PDF, ePUB
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.
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
Who This Book Is For
Machine learning developers and architects who want to stay ahead of the curve in the field of AI and deep 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 Gradient and Q-Learning
Chapter 9: Integrated Planning and Learning
Chapter 10: Further Exploration and Next Steps