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title: Software quality analysis of RL programs | ||
period: 2024 | ||
level: master | ||
area: ai | ||
abstract: | ||
people: Nicolas Cardozo | ||
file: rl_analysis | ||
layout: default | ||
--- | ||
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### Context | ||
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The AI hype is starting to find its limits. A reason for these limits comes from the lack of appropriate programming models to represent and express the concepts of learning algorithms. One example of this appears in Reinforcement Learning (RL) [1] programs, which often lack the standards and quality od regular software projets. | ||
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This problem arises, in part, from the poor tools to express and represent the programs built using RL techniques. | ||
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### Project proposal | ||
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In this project we are going to implement an analysis framework tool to help use reason about RL programs in terms of their execution [2], being able to pause and modify program values and code, and to analyze the quality in terms of software metrics. | ||
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### Implementation plan | ||
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- Implement and IDE for RL program analysis | ||
- Integrate execution analysis for RL programs | ||
- Integrate a quality analysis for RL programs | ||
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### Background and Literature | ||
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- [1] [RL](http://incompleteideas.net/book/the-book-2nd.html) | ||
- [2] [Delorean.js](https://peerj.com/articles/cs-1238/) | ||
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### Contact | ||
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n.cardozo |