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Introspection-based Explainable Reinforcement Learning in Episodic and Non-episodic Scenarios

arXiv 2211.12930 ↗First posted 23 Nov 2022cs.RO cs.AIcited by 0

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Abstract (arXiv)

With the increasing presence of robotic systems and human-robot environments in today's society, understanding the reasoning behind actions taken by a robot is becoming more important. To increase this understanding, users are provided with explanations as to why a specific action was taken. Among other effects, these explanations improve the trust of users in their robotic partners. One option for creating these explanations is an introspection-based approach which can be used in conjunction with reinforcement learning agents to provide probabilities of success. These can in turn be used to reason about the actions taken by the agent in a human-understandable fashion. In this work, this introspection-based approach is developed and evaluated further on the basis of an episodic and a non-episodic robotics simulation task. Furthermore, an additional normalization step to the Q-values is proposed, which enables the usage of the introspection-based approach on negative and comparatively small Q-values. Results obtained show the viability of introspection for episodic robotics tasks and, additionally, that the introspection-based approach can be used to generate explanations for the actions taken in a non-episodic robotics environment as well.

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References · 14

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  2. 2017Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networkscited by 936
  3. 2018Programmatically Interpretable Reinforcement Learningcited by 192
  4. 2020Explainable Reinforcement Learning: A Surveycited by 139
  5. 2017Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learningcited by 77
  6. 2017Interpretable Policies for Reinforcement Learning by Genetic Programmingcited by 47
  7. 2019Explainability in Human-Agent Systemscited by 34
  8. 2021Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Surveycited by 24
  9. 2020Explainable Goal-Driven Agents and Robots -- A Comprehensive Reviewcited by 23
  10. 2020Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenariocited by 19
  11. 2019Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agentscited by 15
  12. 2020A Comparison of Humanoid Robot Simulators: A Quantitative Approachcited by 8
2 more
  1. 2022Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarioscited by 5
  2. 2021Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Taskcited by 2

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