'Programmatically interpretable reinforcement learning' bookmarked by ~acolyer

Programmatically interpretable reinforcement learning

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Programmatically interpretable reinforcement learning, Verma et al., ICML 2018 Being able to trust (interpret, verify) a controller learned through reinforcement learning (RL) is one of the key challenges for real-world deployments of RL that we looked at earlier this week. It’s also an essential requirement for agents in human-machine collaborations (i.e, all deployments at some … Continue reading Programmatically interpretable reinforcement learning

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Synthesizing data structure transformations from input-output examples

acolyer.org

Synthesizing data structure transformations from input-output examples, Feser et al., PLDI’15 The Programmatically Interpretable Reinforcement Learning...
Synthesizing data structure transformations from input-output examples, Feser et al., PLDI’15 The Programmatically Interpretable Reinforcement Learning paper that we looked at last time out contained this passing comment coupled with a link to today’s paper choice: It is known from prior work that such [functional] languages offer natural advantages in program synthesis. That certainly caught … Continue reading Synthesizing data structure transformations from input-output examples
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Challenges of real-world reinforcement learning

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Challenges of real-world reinforcement learning, Dulac-Arnold et al., ICML’19 Last week we looked at some of...
Challenges of real-world reinforcement learning, Dulac-Arnold et al., ICML’19 Last week we looked at some of the challenges inherent in automation and in building systems where humans and software agents collaborate. When we start talking about agents, policies, and modelling the environment, my thoughts naturally turn to reinforcement learning (RL). Today’s paper choice sets out … Continue reading Challenges of real-world reinforcement learning
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

acolyer.org

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead...
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead Rudin et al., arXiv 2019 With thanks to Glyn Normington for pointing out this paper to me. It’s pretty clear from the title alone what Cynthia Rudin would like us to do! The paper is a mix of technical … Continue reading Stop explaining black box machine learning...
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