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ORCIDSriram Ganapathi Subramanian
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2020 – today
- 2024
[c11]Sriram Ganapathi Subramanian, Guiliang Liu, Mohammed Elmahgiubi, Kasra Rezaee, Pascal Poupart:
Confidence Aware Inverse Constrained Reinforcement Learning. ICML 2024- [i13]Agustinus Kristiadi, Felix Strieth-Kalthoff, Sriram Ganapathi Subramanian, Vincent Fortuin, Pascal Poupart, Geoff Pleiss:
How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization? CoRR abs/2406.06459 (2024) - [i12]Sriram Ganapathi Subramanian, Guiliang Liu, Mohammed Elmahgiubi, Kasra Rezaee, Pascal Poupart:
Confidence Aware Inverse Constrained Reinforcement Learning. CoRR abs/2406.16782 (2024) - [i11]Guiliang Liu, Sheng Xu, Shicheng Liu, Ashish Gaurav, Sriram Ganapathi Subramanian, Pascal Poupart:
A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges. CoRR abs/2409.07569 (2024) - 2023
- [j4]Su Zhang, Srijita Das, Sriram Ganapathi Subramanian, Matthew E. Taylor:
Two-Level Actor-Critic Using Multiple Teachers. Trans. Mach. Learn. Res. 2023 (2023) - [c10]Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley:
Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning. AAMAS 2023: 1144-1153 - [c9]Su Zhang, Srijita Das, Sriram Ganapathi Subramanian, Matthew E. Taylor:
Two-Level Actor-Critic Using Multiple Teachers. AAMAS 2023: 2589-2591 - [c8]Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley:
Multi-Agent Advisor Q-Learning (Extended Abstract). IJCAI 2023: 6884-6889 - [i10]Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson
, Mark Crowley:
Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning. CoRR abs/2301.11153 (2023) - [i9]Chris Beeler, Sriram Ganapathi Subramanian, Kyle Sprague, Nouha Chatti, Colin Bellinger, Mitchell Shahen, Nicholas Paquin, Mark Baula, Amanuel Dawit, Zihan Yang, Xinkai Li, Mark Crowley, Isaac Tamblyn:
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry. CoRR abs/2305.14177 (2023) - 2022
- [j3]Ken Ming Lee, Sriram Ganapathi Subramanian, Mark Crowley:
Investigation of independent reinforcement learning algorithms in multi-agent environments. Frontiers Artif. Intell. 5 (2022) - [j2]Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley:
Multi-Agent Advisor Q-Learning. J. Artif. Intell. Res. 74: 1-74 (2022) - [c7]Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart:
Decentralized Mean Field Games. AAAI 2022: 9439-9447 - 2021
- [c6]Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart:
Partially Observable Mean Field Reinforcement Learning. AAMAS 2021: 537-545 - [i8]Volodymyr Tkachuk, Sriram Ganapathi Subramanian, Matthew E. Taylor:
The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning. CoRR abs/2103.04416 (2021) - [i7]Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley:
Multi-Agent Advisor Q-Learning. CoRR abs/2111.00345 (2021) - [i6]Ken Ming Lee, Sriram Ganapathi Subramanian, Mark Crowley:
Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments. CoRR abs/2111.01100 (2021) - [i5]Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart:
Decentralized Mean Field Games. CoRR abs/2112.09099 (2021) - 2020
- [c5]Sushrut Bhalla, Sriram Ganapathi Subramanian, Mark Crowley:
Deep Multi Agent Reinforcement Learning for Autonomous Driving. Canadian AI 2020: 67-78 - [c4]Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor, Nidhi Hegde:
Multi Type Mean Field Reinforcement Learning. AAMAS 2020: 411-419 - [i4]Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor, Nidhi Hegde:
Multi Type Mean Field Reinforcement Learning. CoRR abs/2002.02513 (2020) - [i3]Piyush Jain, Sean C. P. Coogan, Sriram Ganapathi Subramanian, Mark Crowley, Steve Taylor, Mike D. Flannigan:
A review of machine learning applications in wildfire science and management. CoRR abs/2003.00646 (2020) - [i2]Sai Krishna Gottipati, Yashaswi Pathak, Rohan Nuttall, Sahir, Raviteja Chunduru, Ahmed Touati, Sriram Ganapathi Subramanian, Matthew E. Taylor, Sarath Chandar:
Maximum Reward Formulation In Reinforcement Learning. CoRR abs/2010.03744 (2020) - [i1]Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart:
Partially Observable Mean Field Reinforcement Learning. CoRR abs/2012.15791 (2020)
2010 – 2019
- 2019
- [c3]Sushrut Bhalla, Sriram Ganapathi Subramanian, Mark Crowley:
Training Cooperative Agents for Multi-Agent Reinforcement Learning. AAMAS 2019: 1826-1828 - 2018
- [j1]Sriram Ganapathi Subramanian, Mark Crowley:
Using Spatial Reinforcement Learning to Build Forest Wildfire Dynamics Models From Satellite Images. Frontiers ICT 5: 6 (2018) - [c2]Sriram Ganapathi Subramanian, Mark Crowley:
Combining MCTS and A3C for Prediction of Spatially Spreading Processes in Forest Wildfire Settings. Canadian AI 2018: 285-291 - [c1]Sriram Ganapathi Subramanian, Jaspreet Singh Sambee, Benyamin Ghojogh, Mark Crowley:
Decision Assist for Self-driving Cars. Canadian AI 2018: 381-387
Coauthor Index
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