Joelle Pineau, a Canadian computer scientist and associate professor at McGill University where she co-directs the Reasoning and Learning Lab [1]. Her research focuses on planning under uncertainty and Bayesian reinforcement learning in the domain of Partially Observable Markov Decision Processes (POMDPs) along with robotics. She received her Ph.D. on Tractable Planning Under Uncertainty in 2004 at Carnegie Mellon University under Geoffrey Gordon and Sebastian Thrun [2].
Joelle Pineau [3]

Selected Publications

2002 ...

• Joelle Pineau, Sebastian Thrun (2002). An integrated approach to hierarchy and abstraction for POMDPs. CMU-RI-TR-02-21, Carnegie Mellon University, pdf
• Joelle Pineau, Geoffrey Gordon, Sebastian Thrun (2003). Point-based value iteration: An anytime algorithm for POMDPs. IJCAI, pdf
• Joelle Pineau (2004). Tractable Planning Under Uncertainty: Exploiting Structure. Ph.D. thesis, Carnegie Mellon University, pdf
• Robert West, Joelle Pineau, Doina Precup (2009). Wikispeedia: An Online Game for Inferring Semantic Distances between Concepts. pdf ^[5]

2010 ...

• Mohammad Ghavamzadeh, Shie Mannor, Joelle Pineau, Aviv Tamar (2016). Bayesian Reinforcement Learning: A Survey. arXiv:1609.04436
• Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger (2017). Deep Reinforcement Learning that Matters. arXiv:1709.06560

External Links

• Joelle Pineau's Home
• Joelle Pineau - CIFAR
• Joelle Pineau - School of Computer Science, McGill University - VideoLectures.NET
• Joelle Pineau - Google Scholar Citations
• Joelle Pineau - The Mathematics Genealogy Project

References

1. ^ Reasoning and Learning Lab
2. ^ Joelle Pineau (2004). Tractable Planning Under Uncertainty: Exploiting Structure. Ph.D. thesis, Carnegie Mellon University, pdf
3. ^ Joelle Pineau's Home
4. ^ dblp: Joelle Pineau
5. ^ Wikispeedia

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