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@InProceedings{ALA2021-BHARATH,
  author = {Bharath Masetty and Reuth Mirsky and Ashish D. Deshpande and Michael Mauk and Peter Stone},
  title = {Is the Cerebellum a Model-Based Reinforcement Learning Agent?},
  booktitle = {Adaptive and Learning Agents Workshop at AAMAS},
  location = {Virtual},
  month = {May},
  year = {2021},
  abstract = {
  The cerebellum is an integral part of the human brain and understanding
  its role in learning might present an opportunity for reciprocal enrichment
  of the fields of artificial intelligence and neuroscience. In this paper, 
  we present a novel idea that the cerebellum's role goes beyond functioning
  as a supervised learning machine to performing model-based reinforcement
  learning. We revisit the current theories about the cerebellum's role in
  human learning processes and propose a novel way of evaluating it in the
  context of the simulated cerebellum. Based on the recent experimental
  findings, we propose that the cerebellum performs model-based reinforcement
  learning and we propose a way to evaluate the hypothesis using a simulated
  cerebellum. Finally, we discuss the necessary conditions to evaluate the
  hypothesis and the potential implications for future research of the
  hypothesis holds. 
  },
  wwwnote = {<a href="https://youtu.be/yQ7wM_W8Q8s">Video presentation</a>}
}