Members of the UWECEML lab have had a good couple months, with a few notable papers accepted to great venues.
AAAI Paper on Decentralized Multi-Agent Reinforcement Learning
At this year’s AAAI Conference on Artificial Intelligence in (location) Sriram Ganapathi Subramanian presented the paper:
Ganapathi Subramanian, S., Taylor, M., Crowley, M., & Poupart, P. (2022). Decentralized Mean Field Games. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2022), 36(9), 9439–9447. https://doi.org/https://doi.org/10.1609/aaai.v36i9.21176
You can read more about the research from this post about the paper by one of the other co-authors Prof. Matt Taylor at the University of Alberta.
Canadian AI 2022
Bellinger, C., Drozdyuk, A., Crowley, M., & Tamblyn, I. (2022). Balancing Information with Observation Costs in Deep Reinforcement Learning. Canadian Conference on Artificial Intelligence, 12. https://caiac.pubpub.org/pub/0jmy7gpd
This paper (Bellinger et al., 2022) is called “Balancing Information with Observation Costs in Deep Reinforcement Learning” and it builds on other work (Beeler et al., 2022), (Bellinger et al., 2022), (Bellinger et al., 2021) related to digital chemistry and material design where we attempt to use Reinforcement Learning to come up with better pathways for materials. This is a collaboration with the NRC. You can see the project page for ChemGymRL for more information.
Lee, K. M., Ganapathi Subramanian, S., & Crowley, M. (2021). Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments. NeurIPS 2021 Deep Reinforcement Learning Workshop, 15.
This workshop paper (Lee et al., 2021) and presentation was part of a project for undergraduate student Ken Ming Lee, who has worked in the lab as a URA multiple terms on RL algorithms and software development. The original idea to do an empirical study of RL algorithms in Multiagent setting flowed out of results needed for Sriram’s PhD research. This paper is an empirical comparison of many single and multi-agent algorithms on a range of multi-agent planning domains. A longer version of this work has been submitted to Frontiers in AI journal for consideration (Lee et al., 2022).
- Balancing Information with Observation Costs in Deep Reinforcement LearningIn Canadian Conference on Artificial Intelligence Canadian Artificial Intelligence Association (CAIAC), Toronto, Ontario, Canada, may, 2022.
- SubWorldDynamic programming with partial information to overcome navigational uncertainty in a nautical environmentIEEE Intelligent Systems. IEEE, 2022.
- Scientific Discovery and the Cost of Measurement – Balancing Information and Cost in Reinforcement LearningIn 1st Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE) feb, 2022.
- AmrlActive Measure Reinforcement Learning for Observation Cost Minimization: A framework for minimizing measurement costs in reinforcement learningIn Canadian Conference on Artificial Intelligence Springer, 2021.
- MARLEmpircalInvestigation of Independent Reinforcement Learning Algorithms in Multi-Agent EnvironmentsIn NeurIPS 2021 Deep Reinforcement Learning Workshop dec, 2021.
- MARLEmpircalInvestigation of Independent Reinforcement Learning Algorithms in Multi-Agent EnvironmentsFrontiers in Artificial Intelligence. 2022.