You're reading from Reinforcement Learning Algorithms with Python

Product typeBook

Published inOct 2019

Reading LevelBeginner

PublisherPackt

ISBN-139781789131116

Edition1st Edition

Languages

Python

Concepts

Reinforcement Learning

Author (1)

Andrea Lonza

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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Author (1)

Andrea Lonza

Andrea Lonza is a deep learning engineer with a great passion for artificial intelligence and a desire to create machines that act intelligently. He has acquired expert knowledge in reinforcement learning, natural language processing, and computer vision through academic and industrial machine learning projects. He has also participated in several Kaggle competitions, achieving high results. He is always looking for compelling challenges and loves to prove himself.
Read more about Andrea Lonza

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