Demystifying Reinforcement Learning in Production Scheduling via Explainable AI

• URL: http://arxiv.org/abs/2408.09841v2
• Date: Fri, 30 Aug 2024 10:28:53 GMT
• Title: Demystifying Reinforcement Learning in Production Scheduling via Explainable AI
• Authors: Daniel Fischer, Hannah M. Hüsener, Felix Grumbach, Lukas Vollenkemper, Arthur Müller, Pascal Reusch,
• Abstract summary: Deep Reinforcement Learning (DRL) is a frequently employed technique to solve scheduling problems. Although DRL agents ace at delivering viable results in short computing times, their reasoning remains opaque. We apply two explainable AI (xAI) frameworks to describe the reasoning behind scheduling decisions of a specialized DRL agent in a flow production.
• Score: 0.7515066610159392
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep Reinforcement Learning (DRL) is a frequently employed technique to solve scheduling problems. Although DRL agents ace at delivering viable results in short computing times, their reasoning remains opaque. We conduct a case study where we systematically apply two explainable AI (xAI) frameworks, namely SHAP (DeepSHAP) and Captum (Input x Gradient), to describe the reasoning behind scheduling decisions of a specialized DRL agent in a flow production. We find that methods in the xAI literature lack falsifiability and consistent terminology, do not adequately consider domain-knowledge, the target audience or real-world scenarios, and typically provide simple input-output explanations rather than causal interpretations. To resolve this issue, we introduce a hypotheses-based workflow. This approach enables us to inspect whether explanations align with domain knowledge and match the reward hypotheses of the agent. We furthermore tackle the challenge of communicating these insights to third parties by tailoring hypotheses to the target audience, which can serve as interpretations of the agent's behavior after verification. Our proposed workflow emphasizes the repeated verification of explanations and may be applicable to various DRL-based scheduling use cases.

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)
• Analyzing Adversarial Inputs in Deep Reinforcement Learning [53.3760591018817]
  We present a comprehensive analysis of the characterization of adversarial inputs, through the lens of formal verification. We introduce a novel metric, the Adversarial Rate, to classify models based on their susceptibility to such perturbations. Our analysis empirically demonstrates how adversarial inputs can affect the safety of a given DRL system with respect to such perturbations.
  arXiv  Detail & Related papers  (2024-02-07T21:58:40Z)
• Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation [79.22678026708134]
  In this paper, we propose an inherently interpretable method, named Transferable Prototype Learning ( TCPL) To achieve this goal, we design a hierarchically prototypical module that transfers categorical basic concepts from the source domain to the target domain and learns domain-shared prototypes for explaining the underlying reasoning process. Comprehensive experiments show that the proposed method can not only provide effective and intuitive explanations but also outperform previous state-of-the-arts.
  arXiv  Detail & Related papers  (2023-10-12T06:36:41Z)
• Unveiling the Potential of Counterfactuals Explanations in Employability [0.0]
  We show how counterfactuals are applied to employability-related problems involving machine learning algorithms. The use cases presented go beyond the mere application of counterfactuals as explanations.
  arXiv  Detail & Related papers  (2023-05-17T09:13:53Z)
• 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)
• Explainable Data-Driven Optimization: From Context to Decision and Back Again [76.84947521482631]
  Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. We introduce a counterfactual explanation methodology tailored to explain solutions to data-driven problems. We demonstrate our approach by explaining key problems in operations management such as inventory management and routing.
  arXiv  Detail & Related papers  (2023-01-24T15:25:16Z)
• Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities [2.0341936392563063]
  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.
  arXiv  Detail & Related papers  (2022-10-21T09:50:53Z)
• Explainable Reinforcement Learning via Model Transforms [18.385505289067023]
  We argue that even if the underlying Markov Decision Process is not fully known, it can nevertheless be exploited to automatically generate explanations. We suggest using formal MDP abstractions and transforms, previously used in the literature for expediting the search for optimal policies, to automatically produce explanations.
  arXiv  Detail & Related papers  (2022-09-24T13:18:06Z)
• Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies [79.60322329952453]
  We show how to develop interpretable representations of how agents make decisions. By understanding the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem. We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them. Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time.
  arXiv  Detail & Related papers  (2022-03-14T17:40:42Z)
• Decision Rule Elicitation for Domain Adaptation [93.02675868486932]
  Human-in-the-loop machine learning is widely used in artificial intelligence (AI) to elicit labels from experts. In this work, we allow experts to additionally produce decision rules describing their decision-making. We show that decision rule elicitation improves domain adaptation of the algorithm and helps to propagate expert's knowledge to the AI model.
  arXiv  Detail & Related papers  (2021-02-23T08:07:22Z)
• 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.