Markov Decision Processes

Markov Decision Processes (MDPs) are a mathematical language for definiing the problem of making decisions over time using only the current observations and knowledge.

Our Papers on MDP

  1. PAC Optimal MDP Planning with Application to Invasive Species Management
    Majid Alkaee Taleghan, Thomas G. Dietterich, Mark Crowley, Kim Hall, and H. Jo Albers.
    Journal of Machine Learning Research. 16, 2015.
  2. Using equilibrium policy gradients for spatiotemporal planning in forest ecosystem management
    markcrowley.
    IEEE Transactions on Computers. 63, (1). IEEE computer Society Digital Library. IEEE Computer Society., 2014.
  3. PAC Optimal Planning for Invasive Species Management: Improved Exploration for Reinforcement Learning from Simulator-Defined MDPs
    Thomas G Dietterich, Majid Alkaee Taleghan, and Mark Crowley
    In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2013). Bellevue, WA, USA. 2013.
  4. Cyclic causal models with discrete variables: Markov chain equilibrium semantics and sample ordering
    David Poole, and Mark Crowley
    In International Joint Conference on Artificial Intelligence (IJCAI). Beijing, China. 2013.
  5. Policy gradient planning for environmental decision making with existing simulators
    Mark Crowley, and David Poole.
    In Proceedings of the Twenty-Fifth Conference on Artificial Intelligence (AAAI). San Francisco. 2011.
  6. phd-thesis
    Equilibrium Policy Gradients for Spatiotemporal Planning
    markcrowley.
    UBC Library, Vancouver, BC, Canada.. 2011.
  7. Seeing the Forest Despite the Trees : Large Scale Spatial-Temporal Decision Making
    Mark Crowley, John Nelson, and David Poole.
    In Conference on Uncertainty in Artificial Intelligence (UAI09). Montreal, Canada. 2009.