• Corpus ID: 238407912

Nested Policy Reinforcement Learning

@article{Mandyam2021NestedPR,
  title={Nested Policy Reinforcement Learning},
  author={Aishwarya Mandyam and Andrew Jones and Krzysztof Laudanski and Barbara E. Engelhardt},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.02879}
}
Off-policy reinforcement learning (RL) has proven to be a powerful framework for guiding agents’ actions in environments with stochastic rewards and unknown or noisy state dynamics. In many real-world settings, these agents must operate in multiple environments, each with slightly different dynamics. For example, we may be interested in developing policies to guide medical treatment for patients with and without a given disease, or policies to navigate curriculum design for students with and… 

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