Adaptation Through Learning: Using Machine Learning to Improve Forest Wildfire Management Thursday, June 18, 2020:
Subramanian, S.Ganapthi, Bhalla, S. & Crowley, M., 2019. Learning Multi-Agent Communication with Reinforcement Learning. In Conference on Reinforcement Learning and Decision Making (RLDM-19). Montreal, Canada., p. 4.
Bhalla, S., Subramanian, S.G. & Crowley, M., 2019. Training Cooperative Agents for Multi-Agent Reinforcement Learning. In Proc. of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019). Montreal, Canada.
Deep Learning and back-propagation has been successfully used to perform centralized training with communication protocols among multiple agents in a cooperative environment. In this paper we present techniques for centralized training of Multi-Agent (Deep) Reinforcement Learning (MARL) using the model-free Deep Q-Network as the baseline model and message sharing between agents. We present a novel, scalable, centralized MARL training technique, which separates the message learning module from the policy module. The separation of these modules helps in faster convergence in complex domains like autonomous driving simulators. A second contribution uses the centrally trained model to bootstrap training of distributed, independent, cooperative agent policies for execution and thus addresses the challenges of noise and communication bottlenecks in real-time communication channels. This paper theoretically and empirically compares our centralized training algorithms to current research in the field of MARL. We also present and release a new OpenAI-Gym environment which can be used for multi-agent research as it simulates multiple autonomous cars driving cooperatively on a highway.
Adaptation Through Learning : Using Machine Learning to Improve Forest Wildfire Management, at Department of Electrical Engineering & Computer Science and Engineering, York University, Wednesday, February 13, 2019
Adaptation Through Learning : Using Machine Learning to Improve Forest Wildfire Management, at San Francisco, California, Thursday, January 24, 2019
Fighting Fire with AI: Why AI is Different...and How., at Vancouver, BC, Friday, October 12, 2018
Fighting Fire with AI: Using Artificial Intelligence to Improve Modelling and Decision Making in Wildfire Management, at Banff International Research Station, Banff, Alberta, Canada, Friday, November 17, 2017:
Using Deep Learning and Reinforcement Learning to Tame Spatially Spreading Processes, at University of Waterloo, Wednesday, October 25, 2017
BIRC Workshop On Deep Learning In Medicine, at University Hospital, London, Ontario, Canada, Monday, August 28, 2017: