| Gridin I. Practical Deep Reinforcement Learning with Python 2022.pdf | 8.04 MB |
Textbook in PDF format
Introducing Practical Smart Agents Development using Python, PyTorch, and TensorFlow
Key Features
Exposure to well-known RL techniques, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical.
Hands-on experience with TensorFlow and PyTorch on Reinforcement Learning projects.
Everything is concise, up-to-date, and visually explained with simplified mathematics.
Introducing Reinforcement Learning
Playing Monopoly and Markov Decision Process
Training in Gym
Struggling with Multi-Armed Bandits
Blackjack in Monte Carlo
Escaping Maze with Q-Learning
Discretization
Deep Reinforcement Learning
TensorFlow, PyTorch, and Your First Neural Network
Deep Q-Network and Lunar Lander
Defending Atlantis with Double Deep Q-Network
From Q-Learning to Policy-Gradient
Stock Trading with Actor-Critic
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