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Reinforcement Learning Algorithms with Python

You're reading from   Reinforcement Learning Algorithms with Python Learn, understand, and develop smart algorithms for addressing AI challenges

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Product type Paperback
Published in Oct 2019
Publisher Packt
ISBN-13 9781789131116
Length 366 pages
Edition 1st Edition
Languages
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Author (1):
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Andrea Lonza Andrea Lonza
Author Profile Icon Andrea Lonza
Andrea Lonza
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Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1: Algorithms and Environments FREE CHAPTER
2. The Landscape of Reinforcement Learning 3. Implementing RL Cycle and OpenAI Gym 4. Solving Problems with Dynamic Programming 5. Section 2: Model-Free RL Algorithms
6. Q-Learning and SARSA Applications 7. Deep Q-Network 8. Learning Stochastic and PG Optimization 9. TRPO and PPO Implementation 10. DDPG and TD3 Applications 11. Section 3: Beyond Model-Free Algorithms and Improvements
12. Model-Based RL 13. Imitation Learning with the DAgger Algorithm 14. Understanding Black-Box Optimization Algorithms 15. Developing the ESBAS Algorithm 16. Practical Implementation for Resolving RL Challenges 17. Assessments
18. Other Books You May Enjoy

Assessments

Chapter 3

  • What's a stochastic policy?
    • It's a policy defined in terms of a probability distribution
  • How can a return be defined in terms of the return at the next time step?
  • Why is the Bellman equation so important?
    • Because it provides a general formula to compute the value of a state using the current reward and the value of the subsequent state.
  • Which are the limiting factors of DP algorithms?
    • Due to a complexity explosion with the number of states, they have to be limited. The other constraint is that the dynamics of the system have to be fully known.
  • What's policy evaluation?
    • Is an iterative method to compute the value function for a given policy using the Bellman equations.
  • How does policy iteration and value iteration differs?
    • Policy iteration alternate between policy evaluation and policy improvement, value iteration instead...
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