Portrait of Aditya Mahajan

Aditya Mahajan

Associate Academic Member
Associate Professor, McGill University, Department of Electrical and Computer Engineering
Research Topics
Reinforcement Learning
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Biography

Aditya Mahajan is a professor in the Department of Electrical and Computer Engineering at McGill University and an associate academic member of Mila – Quebec Artificial Intelligence Institute.

He is also a member of the McGill Centre for Intelligent Machines (CIM), the International Laboratory for Learning Systems (ILLS), and the Group for Research in Decision Analysis (GERAD). Mahajan received his BTech degree in electrical engineering from the Indian Institute of Technology Kanpur, and his MSc and PhD degrees in electrical engineering and computer science from the University of Michigan at Ann Arbor.

He is a senior member of the U.S. Institute of Electrical and Electronics Engineers (IEEE), as well as a member of Professional Engineers Ontario. He currently serves as associate editor for IEEE Transactions on Automatic Control, IEEE Control Systems Letters, and Mathematics of Control, Signals, and Systems (Springer). He served as associate editor for the conference editorial board of the IEEE Control Systems Society from 2014 to 2017.

Mahajan’s numerous awards include the 2015 George Axelby Outstanding Paper Award, 2016 NSERC Discovery Accelerator Award, 2014 CDC Best Student Paper Award (as supervisor), and 2016 NecSys Best Student Paper Award (as supervisor). Mahajan’s principal research interests are stochastic control and reinforcement learning.

Current Students

Reza Alvandi
Master's Research - McGill University
Berk Bozkurt
Collaborating Alumni - McGill University
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Arka Ian Goswami
Master's Research - McGill University
Nguyen Nguyen
Master's Research - Université de Montréal
Samin Nili
PhD - McGill University
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Reza Pourmohammadi Najafabadi
Master's Research - McGill University
Github
Amit Sinha
PhD - McGill University
Github
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Tao Zhang
PhD - McGill University

Publications

Dynamic spectrum access under partial observations: A restless bandit approach
Nima Akbarzadeh
Aditya Mahajan
We consider a communication system where multiple unknown channels are available for transmission. Each channel is a channel with state whic… (see more)h evolves in a Markov manner. The transmitter has to select L channels to use and also decide the resources (e.g., power, rate, etc.) to use for each of the selected channels. It observes the state of the channels it uses and receives no feedback on the state of the other channels. We model this problem as a partially observable Markov decision process and obtain a simplified belief state. We show that the optimal resource allocation policy can be identified in closed form. Once the optimal resource allocation policy is fixed, choosing the channel scheduling policy may be viewed as a restless bandit. We present an efficient algorithm to check indexability and compute the Whittle index for each channel. When the model is indexable, the Whittle index policy, which transmits over the L channels with the smallest Whittle indices, is an attractive heuristic policy.
2019-06-01
Canadian Workshop on Information Theory (published)
doi.org
Multi-Agent Estimation and Filtering for Minimizing Team Mean-Squared Error
Mohammad Afshari
Aditya Mahajan
Motivated by estimation problems arising in autonomous vehicles and decentralized control of unmanned aerial vehicles, we consider multi-age… (see more)nt estimation and filtering problems in which multiple agents generate state estimates based on decentralized information and the objective is to minimize a coupled mean-squared error which we call team mean-square error. We call the resulting estimates as minimum team mean-squared error (MTMSE) estimates. We show that MTMSE estimates are different from minimum mean-squared error (MMSE) estimates. We derive closed-form expressions for MTMSE estimates, which are linear function of the observations where the corresponding gain depends on the weight matrix that couples the estimation error. We then consider a filtering problem where a linear stochastic process is monitored by multiple agents which can share their observations (with delay) over a communication graph. We derive expressions to recursively compute the MTMSE estimates. To illustrate the effectiveness of the proposed scheme we consider an example of estimating the distances between vehicles in a platoon and show that MTMSE estimates significantly outperform MMSE estimates and consensus Kalman filtering estimates.
2019-03-28
ArXiv (preprint)
doi.org
arxiv.org
Reinforcement Learning in Stationary Mean-field Games
Jayakumar Subramanian
Aditya Mahajan
Multi-agent reinforcement learning has made significant progress in recent years, but it remains a hard problem. Hence, one often resorts to… (see more) developing learning algorithms for specific classes of multi-agent systems. In this paper we study reinforcement learning in a specific class of multi-agent systems systems called mean-field games. In particular, we consider learning in stationary mean-field games. We identify two different solution concepts---stationary mean-field equilibrium and stationary mean-field social-welfare optimal policy---for such games based on whether the agents are non-cooperative or cooperative, respectively. We then generalize these solution concepts to their local variants using bounded rationality based arguments. For these two local solution concepts, we present two reinforcement learning algorithms. We show that the algorithms converge to the right solution under mild technical conditions and demonstrate this using two numerical examples.
2019-03-01
Adaptive Agents and Multi-Agent Systems (published)
dblp.uni-trier.de