Publications by Kamalaruban Parameswaran

Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch

Volkan Cevher, Kamalaruban Parameswaran, Luca Viano, Yu-Ting Huang

We study the inverse reinforcement learning (IRL) problem under a transition dynamics mismatch between the expert and the learner. Specifically, we consider the Maximum Causal Entropy (MCE) IRL learner model and provide a tight upper bound on the learner’s ...

2021

Optimization for Reinforcement Learning: From a single agent to cooperative agents

Volkan Cevher, Kamalaruban Parameswaran

Fueled by recent advances in deep neural networks, reinforcement learning (RL) has been in the limelight because of many recent breakthroughs in artificial intelligence, including defeating humans in games (e.g., chess, Go, StarCraft), self-driving cars, s ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Kamalaruban Parameswaran, Yu-Ting Huang, Cheng Shi

We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents. Leveraging the powerful Stochastic Gradient Langevin Dynamics, we present a novel, scalable two-player RL algorithm, which is a sampli ...

34th Conference on Neural Information Processing Systems (NeurIPS 2020)2020

Interactive Teaching Algorithms for Inverse Reinforcement Learning

Volkan Cevher, Kamalaruban Parameswaran

We study the problem of inverse reinforcement learning (IRL) with the added twist that the learner is assisted by a helpful teacher. More formally, we tackle the following algorithmic question: How could a teacher provide an informative sequence of demonst ...

2019

Interactive Teaching Algorithms for Inverse Reinforcement Learning

Volkan Cevher, Kamalaruban Parameswaran

We study the problem of inverse reinforcement learning (IRL) with the added twist that the learner is assisted by a helpful teacher. More formally, we tackle the following algorithmic question: How could a teacher provide an informative sequence of demonst ...

2019

Iterative Classroom Teaching

Pierre Dillenbourg, Volkan Cevher, Thibault Lucien Christian Asselborn, Louis Pierre Faucon, Shuqing Teresa Yeo, Kamalaruban Parameswaran

We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students. Their diversity stems from differences in their initial internal states as well as their learning rate ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2019