Reinforcement Learning

RL is the study of learning decision making policies from experience with computers.

One of my core research areas is into understanding the computational mechanisms that can enable learning to perform complex tasks primarily from experience and feedback. This topic, called Reinforcement Learning, has a complex history tying fields as diverse as neuroscience, behavioural and development psychology, economics and computer science. I approach it as a computational researcher aiming to build Artificial Intelligence agents that learn to way Humans do, not by any correspondence of their “brain” and it “neural” structure by the algorithms they both use to learn to act in a complex, mysterious world.

Learning Resources

Courses and Texts

Seminal Deep RL Papers

  • https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf

Tools

  • https://gym.openai.com/

Our Papers on Reinforcement Learning

  1. Prediction and Causality: How Can Machine Learning be Used for COVID-19?
    Crowley, Mark
    In "What Needs to be done in order to Curb the Spread of Covid-19: Exposure Notification, Legal Considerations, and Statistical Modeling", a Conference on Data and Privacy during a Global Pandemic 2021
  2. Partially Observable Mean Field Reinforcement Learning
    Ganapathi Subramanian, Sriram, Taylor, Matthew, Crowley, Mark, and Poupart, Pascal
    In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) 2021
  3. Active Measure Reinforcement Learning for Observation Cost Minimization: A framework for minimizing measurement costs in reinforcement learning
    Bellinger, Colin, Coles, Rory, Crowley, Mark, and Tamblyn, Isaac
    In Canadian Conference on Artificial Intelligence 2021
  4. Deep Multi Agent Reinforcement Learning for Autonomous Driving
    Bhalla, Sushrut, Ganapathi Subramanian, Sriram, and Crowley, Mark
    In Canadian Conference on Artificial Intelligence 2020
  5. Learning Multi-Agent Communication with Reinforcement Learning
    Bhalla, Sushrut, Ganapathi Subramanian, Sriram, and Crowley, Mark
    In Conference on Reinforcement Learning and Decision Making (RLDM-19) 2019
  6. Training Cooperative Agents for Multi-Agent Reinforcement Learning
    Bhalla, Sushrut, Ganapathi Subramanian, Sriram, and Crowley, Mark
    In Proc. of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019) 2019
  7. Learning Forest Wildfire Dynamics from Satellite Images Using Reinforcement Learning
    Subramanian, Sriram Ganapathi, and Crowley, Mark
    In Conference on Reinforcement Learning and Decision Making 2017