Reinforcement Learning Algorithms with Python

Reinforcement Learning Algorithms with Python will help you master RL algorithms and understand their implementation as you build self-learning agents. Starting with an introduction to the tools, libraries, and setup needed to work in the RL environment, this book covers the building blocks of RL and delves into value-based methods, such as the application of Q-learning and SARSA algorithms. (Limited-time offer)

Table of Contents

• Section 1: Algorithms and Environments
• The Landscape of Reinforcement Learning
• Implementing RL Cycle and OpenAI Gym
• Solving Problems with Dynamic Programming
• Section 2: Model-Free RL Algorithms
• Q-Learning and SARSA Applications
• Deep Q-Network
• Learning Stochastic and PG Optimization
• TRPO and PPO Implementation
• DDPG and TD3 Applications
• Section 3: Beyond Model-Free Algorithms and Improvements
• Model-Based RL
• Imitation Learning with the DAgger Algorithm
• Understanding Black-Box Optimization Algorithms
• Developing the ESBAS Algorithm
• Practical Implementation for Resolving RL Challenges
• Assessments

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