- In Q-learning, what does the Q stand for (you will have to research this on the internet).
- What could we do to limit the number of states that the Q-learning algorithm has to search through?
- What effect does changing the learning rate have on the learning process?
- What function or parameter serves to penalize longer paths in the Q-learning equation? What effect does increasing or decreasing this function have?
- In the genetic algorithm, how would you go about penalizing longer paths so that shorter paths (fewer number of steps) would be preferred?
- Look up the SARSA variation of Q-learning. How would you implement the SARSA technique into program 2.
- What effect does changing the learning rate in the genetic algorithm change? What is the upper and lower bound of the learning rate?
- In the genetic algorithm, what effect does lowering the population cause?
Artificial Intelligence for Robotics
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Artificial Intelligence for Robotics
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Overview of this book
Artificial Intelligence for Robotics starts with an introduction to Robot Operating Systems (ROS), Python, robotic fundamentals, and the software and tools that are required to start out with robotics. You will learn robotics concepts that will be useful for making decisions, along with basic navigation skills.
As you make your way through the chapters, you will learn about object recognition and genetic algorithms, which will teach your robot to identify and pick up an irregular object. With plenty of use cases throughout, you will explore natural language processing (NLP) and machine learning techniques to further enhance your robot. In the concluding chapters, you will learn about path planning and goal-oriented programming, which will help your robot prioritize tasks.
By the end of this book, you will have learned to give your robot an artificial personality using simulated intelligence.
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Current Title:
Artificial Intelligence for Robotics
Table of Contents (13 chapters)
Preface
Free Chapter
Foundation for Advanced Robotics and AI
Setting Up Your Robot
A Concept for a Practical Robot Design Process
Object Recognition Using Neural Networks and Supervised Learning
Picking up the Toys
Teaching a Robot to Listen
Avoiding the Stairs
Putting Things Away
Giving the Robot an Artificial Personality
Conclusions and Reflections
Assessments
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