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  Challenges of real-world reinforcement learning

  

  acolyer.org

  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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  Programmatically interpretable reinforcement learning

  

  acolyer.org

  Programmatically interpretable reinforcement learning, Verma et al., ICML 2018 Being able to trust (interpret, verify) a...

  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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• Grandmaster level in StarCraft II using multi-agent reinforcement learning [pdf]

  

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  Grandmaster level in StarCraft II using multi-agent reinforcement learning

  

  deepmind.com

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