Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling

핵심 개념

Developing IRL to convert DRL policies into interpretable decision trees for effective scheduling in HPC.

초록

This content discusses the development of Interpretable Reinforcement Learning (IRL) to address the lack of interpretability in Deep Reinforcement Learning (DRL) policies for cluster scheduling. The framework aims to convert black-box DNN policies into decision trees, enhancing understanding and practical deployment. Key highlights include:

Introduction to IRL framework for DRL scheduling.
Utilization of imitation learning to train decision trees mimicking DNN agents.
Incorporation of Dataset Aggregation algorithm and critical state concept for efficient tree pruning.
Demonstrated trace-based experiments showcasing comparable performance with enhanced interpretability.
Evaluation metrics include average job wait time and slowdown comparisons across different methods.

통계

"In this work, we present a framework called IRL (Interpretable Reinforcement Learning) to address the issue of interpretability of DRL scheduling."
"Through trace-based experiments, we demonstrate that IRL is capable of converting a black-box DNN policy into an interpretable rule-based decision tree while maintaining comparable scheduling performance."

인용구

"Decision tree is non-parametric, and easy for humans to understand."
"IRL converts a black-box DRL policy to an easy-to-understand decision tree policy."

핵심 통찰 요약

Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling

by Boyang Li,Zh... 게시일 arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16293.pdf

더 깊은 질문

How can the concept of critical state be applied in other areas beyond cluster scheduling

The concept of critical state, as applied in cluster scheduling to reduce the size of decision trees while maintaining effectiveness, can be extended to various other domains beyond scheduling. For instance:

Healthcare: Critical states in patient monitoring systems could help identify key indicators that significantly impact health outcomes, leading to more targeted interventions.
Finance: Identifying critical market conditions or triggers for financial decisions could enhance risk management strategies and investment choices.
Manufacturing: Recognizing critical production stages or quality control checkpoints can optimize processes and minimize defects.
Natural Disasters: Understanding critical environmental factors before disasters occur can improve preparedness and response strategies.
By applying the concept of critical state across diverse fields, organizations can streamline decision-making processes, enhance efficiency, and mitigate risks effectively.

What are potential drawbacks or limitations of converting DNN policies into decision trees

Converting DNN policies into decision trees presents certain drawbacks and limitations:

Loss of Complexity: Decision trees are inherently less complex than deep neural networks. This reduction in complexity may lead to a loss of nuanced patterns and intricate relationships captured by DNNs.

Overfitting Concerns: Decision trees are prone to overfitting when trained on limited data compared to DNNs with their ability to generalize better from vast datasets.

Limited Representational Power: Decision trees might struggle with capturing highly abstract features or non-linear relationships present in the original DNN policy due to their hierarchical structure.

Scalability Challenges: As decision tree size grows exponentially with depth, converting large-scale DNN models into decision trees may result in unwieldy structures that are challenging to manage efficiently.

How might the use of interpretable models like IRL impact the broader adoption of reinforcement learning technologies

The adoption of interpretable models like IRL could have several impacts on the broader acceptance and utilization of reinforcement learning technologies:

Enhanced Trust:

Interpretable models provide transparency into how decisions are made, increasing trust among stakeholders who may be skeptical about using black-box algorithms.

Regulatory Compliance:

In regulated industries such as finance or healthcare, interpretable models facilitate compliance with regulations requiring explanations for algorithmic decisions.

Knowledge Transfer:

Interpretable models make it easier for domain experts without extensive ML expertise to understand and contribute insights towards model development.

5Deployment Confidence:

Organizations hesitant about deploying opaque AI solutions may feel more confident adopting reinforcement learning technologies knowing they have interpretable alternatives available like IRL.

5 .6Error Detection & Debugging:
- The interpretability provided by IRL enables easier identification of errors within the model's logic or training process which aids in debugging efforts
Overall, incorporating interpretable modeling techniques like IRL has the potential not only to improve performance but also accelerate the integration of reinforcement learning methods across various industries by addressing concerns related to transparency and accountability."