CV

Education

• B.S. in GitHub, GitHub University, 2012
• M.S. in Jekyll, GitHub University, 2014
• Ph.D in Version Control Theory, GitHub University, 2018 (expected)

Work experience

• Summer 2015: Research Assistant
  • Github University
  • Duties included: Tagging issues
  • Supervisor: Professor Git
• Fall 2015: Research Assistant
  • Github University
  • Duties included: Merging pull requests
  • Supervisor: Professor Hub

Skills

• Skill 1
• Skill 2
  • Sub-skill 2.1
  • Sub-skill 2.2
  • Sub-skill 2.3
• Skill 3

Publications

Cohort intelligence: a self supervised learning behavior

Anand J Kulkarni, Ishan P Durugkar, Mrinal Kumar. (2013). "Cohort intelligence: a self supervised learning behavior" 2013 IEEE international conference on systems, man, and cybernetics.

Deep reinforcement learning with macro-actions

Ishan P Durugkar, Clemens Rosenbaum, Stefan Dernbach, Sridhar Mahadevan. (2016). "Deep reinforcement learning with macro-actions". arXiv preprint arXiv:1606.04615.

Inverting Variational Autoencoders for Improved Generative Accuracy

Ian Gemp, Ishan Durugkar, Mario Parente, M Darby Dyar, Sridhar Mahadevan. (2016). "Inverting Variational Autoencoders for Improved Generative Accuracy". arXiv preprint arXiv:1608.05983.

Predictive off-policy policy evaluation for nonstationary decision problems, with applications to digital marketing

Philip S Thomas, Georgios Theocharous, Mohammad Ghavamzadeh, Ishan Durugkar, Emma Brunskill. (2017). "Predictive off-policy policy evaluation for nonstationary decision problems, with applications to digital marketing". Twenty-Ninth IAAI Conference.

Generative Multi-Adversarial Networks

Ishan Durugkar, Ian Gemp, Sridhar Mahadevan. (2017). "Generative Multi-Adversarial Networks". International Conference on Learning Representations, 2017.

TD Learning with Constrained Gradients

Ishan Durugkar, Peter Stone. (2017). "TD Learning with Constrained Gradients". Deep Reinforcement Learning Symposium, NeurIPS 2017.

Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning

Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum. (2018). "Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning". International Conference on Learning Representations, 2017.

Multi-Preference Actor Critic

Ishan Durugkar, Matthew Hausknecht, Adith Swaminathan, Patrick MacAlpine. (2019). "Multi-Preference Actor Critic". ArXiv Preprint 2019.

Reducing Sampling Error in Batch Temporal Difference Learning

Brahma S. Pavse, Ishan Durugkar, Josiah P. Hanna, Peter Stone. (2020). "Reducing Sampling Error in Batch Temporal Difference Learning". International Conference on Machine Learning, 2020.

Balancing Individual Preferences and Shared Objectives in Multiagent Reinforcement Learning

Ishan Durugkar, Elad Liebman, Peter Stone. (2020). "Balancing Individual Preferences and Shared Objectives in Multiagent Reinforcement Learning". International Joint Conference on Artificial Intelligence, 2020.

An Imitation from Observation Approach to Sim-to-Real Transfer

Siddarth Desai, Ishan Durugkar, Haresh Karnan, Garrett Warnell, Josiah Hanna, Peter Stone. (2020). "An Imitation from Observation Approach to Sim-to-Real Transfer". NeurIPS, 2020.

Adversarial Intrinsic Motivation for Reinforcement Learning

Ishan Durugkar, Mauricio Tec, Scott Niekum, Peter Stone. (2021). "Adversarial Intrinsic Motivation for Reinforcement Learning". NeurIPS, 2021.

Wasserstein Distance Maximizing Intrinsic Control

Ishan Durugkar, Steven Hansen, Stephen Spencer, Volodymyr Mnih. (2021). "Wasserstein Distance Maximizing Intrinsic Control". Deep RL workshop, NeurIPS, 2021.

DM$^2$: Decentralized Multi-Agent Reinforcement Learning for Distribution Matching

Caroline Wang, Ishan Durugkar, Elad Liebman, Peter Stone. (2023). "DM2: Decentralized Multi-Agent Reinforcement Learning for Distribution Matching". AAAI, 2023.

Talks

Teaching

Service and leadership

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