Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities

• URL: http://arxiv.org/abs/2210.11846v2
• Date: Fri, 9 Feb 2024 15:28:52 GMT
• Title: Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities
• Authors: Jasmina Gajcin and Ivana Dusparic
• Abstract summary: Most explanation methods for AI are focused on developers and expert users. Counterfactual explanations offer users advice on what can be changed in the input for the output of the black-box model to change. Counterfactuals are user-friendly and provide actionable advice for achieving the desired output from the AI system.
• Score: 2.0341936392563063
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While AI algorithms have shown remarkable success in various fields, their lack of transparency hinders their application to real-life tasks. Although explanations targeted at non-experts are necessary for user trust and human-AI collaboration, the majority of explanation methods for AI are focused on developers and expert users. Counterfactual explanations are local explanations that offer users advice on what can be changed in the input for the output of the black-box model to change. Counterfactuals are user-friendly and provide actionable advice for achieving the desired output from the AI system. While extensively researched in supervised learning, there are few methods applying them to reinforcement learning (RL). In this work, we explore the reasons for the underrepresentation of a powerful explanation method in RL. We start by reviewing the current work in counterfactual explanations in supervised learning. Additionally, we explore the differences between counterfactual explanations in supervised learning and RL and identify the main challenges that prevent the adoption of methods from supervised in reinforcement learning. Finally, we redefine counterfactuals for RL and propose research directions for implementing counterfactuals in RL.

Related papers

• Semifactual Explanations for Reinforcement Learning [1.5320737596132754]
  Reinforcement Learning (RL) is a learning paradigm in which the agent learns from its environment through trial and error. Deep reinforcement learning (DRL) algorithms represent the agent's policies using neural networks, making their decisions difficult to interpret. Explaining the behaviour of DRL agents is necessary to advance user trust, increase engagement, and facilitate integration with real-life tasks.
  arXiv  Detail & Related papers  (2024-09-09T08:37:47Z)
• Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing [51.524108608250074]
  Black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in remote sensing. We perform a systematic review to identify the key trends in the field and shed light on novel explainable AI approaches. We also give a detailed outlook on the challenges and promising research directions.
  arXiv  Detail & Related papers  (2024-02-21T13:19:58Z)
• Vision-Language Models Provide Promptable Representations for Reinforcement Learning [67.40524195671479]
  We propose a novel approach that uses the vast amounts of general and indexable world knowledge encoded in vision-language models (VLMs) pre-trained on Internet-scale data for embodied reinforcement learning (RL) We show that our approach can use chain-of-thought prompting to produce representations of common-sense semantic reasoning, improving policy performance in novel scenes by 1.5 times.
  arXiv  Detail & Related papers  (2024-02-05T00:48:56Z)
• RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning [2.0341936392563063]
  We propose RACCER, the first-specific approach to generating counterfactual explanations for the behavior of RL agents. We use a tree search to find the most suitable counterfactuals based on the defined properties. We evaluate RACCER in two tasks as well as conduct a user study to show that RL-specific counterfactuals help users better understand agents' behavior.
  arXiv  Detail & Related papers  (2023-03-08T09:47:00Z)
• GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual Explanations [0.7874708385247353]
  We propose a novel but simple method to generate counterfactual explanations for RL agents. Our method is fully model-agnostic and we demonstrate that it outperforms the only previous method in several computational metrics.
  arXiv  Detail & Related papers  (2023-02-24T15:29:43Z)
• Implicit Offline Reinforcement Learning via Supervised Learning [83.8241505499762]
  Offline Reinforcement Learning (RL) via Supervised Learning is a simple and effective way to learn robotic skills from a dataset collected by policies of different expertise levels. We show how implicit models can leverage return information and match or outperform explicit algorithms to acquire robotic skills from fixed datasets.
  arXiv  Detail & Related papers  (2022-10-21T21:59:42Z)
• Experiential Explanations for Reinforcement Learning [15.80179578318569]
  Reinforcement Learning systems can be complex and non-interpretable. We propose a technique, Experiential Explanations, to generate counterfactual explanations.
  arXiv  Detail & Related papers  (2022-10-10T14:27:53Z)
• Rethinking Learning Dynamics in RL using Adversarial Networks [79.56118674435844]
  We present a learning mechanism for reinforcement learning of closely related skills parameterized via a skill embedding space. The main contribution of our work is to formulate an adversarial training regime for reinforcement learning with the help of entropy-regularized policy gradient formulation.
  arXiv  Detail & Related papers  (2022-01-27T19:51:09Z)
• PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training [94.87393610927812]
  We present an off-policy, interactive reinforcement learning algorithm that capitalizes on the strengths of both feedback and off-policy learning. We demonstrate that our approach is capable of learning tasks of higher complexity than previously considered by human-in-the-loop methods.
  arXiv  Detail & Related papers  (2021-06-09T14:10:50Z)
• Explainability in Deep Reinforcement Learning [68.8204255655161]
  We review recent works in the direction to attain Explainable Reinforcement Learning (XRL) In critical situations where it is essential to justify and explain the agent's behaviour, better explainability and interpretability of RL models could help gain scientific insight on the inner workings of what is still considered a black box.
  arXiv  Detail & Related papers  (2020-08-15T10:11:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.