This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges have impeded the real-world adoption of RL algorithms for robotics and related areas. These include difficulties in preventing safety constraints from being violated and the issues faced by systems operators who desire explainable policies and actions. Robotics applications present a unique set of considerations and result in a number of opportunities related to their physical, real-world sensory input and interactions.
The authors consider classification techniques used in past surveys and papers and attempt to unify terminology across the field. The book provides an in-depth exploration of 12 attributes that can be used to classify explainable/interpretable techniques. These include whether the RL method is model-agnostic or model-specific, self-explainable or post-hoc, as well as additional analysis of the attributes of scope, when-produced, format, knowledge limits, explanation accuracy, audience, predictability, legibility, readability, and reactivity. The book is organized around a discussion of these methods broken down into 42 categories and subcategories, where each category can be classified according to some of the attributes. The authors close by identifying gaps in the current research and highlighting areas for future investigation.| ISBN: | 9783031475207 |
| Publication date: | 20th March 2025 |
| Author: | Aaron M Roth, Dinesh N Manocha, Ram D Sriram, Elham Tabassi |
| Publisher: | Springer an imprint of Springer International Publishing |
| Format: | Paperback |
| Pagination: | 114 pages |
| Series: | Synthesis Lectures on Artificial Intelligence and Machine Learning |
| Genres: |
Artificial intelligence Robotics Automatic control engineering Machine learning Information visualization |
This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges have impeded the real-world adoption of RL algorithms for robotics and related areas.
Explainable and Interpretable Reinforcement Learning for Robotics features in the following genres: Artificial intelligence, Robotics, Automatic control engineering, Machine learning, Information visualization
Paperback, Hardback. £44.99, down from the £49.99 cover price. Not Available.
Explainable and Interpretable Reinforcement Learning for Robotics was written by Aaron M Roth, Dinesh N Manocha, Ram D Sriram, Elham Tabassi and published by Springer an imprint of Springer International Publishing
Explainable and Interpretable Reinforcement Learning for Robotics has 114 pages
Yes it is part of Synthesis Lectures on Artificial Intelligence and Machine Learning series
£44.99, reduced from £49.99. Not Available.