Kamalaruban Parameswaran

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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 ...

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 ...

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 ...

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 ...

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 ...

Kamalaruban Parameswaran

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Iterative Classroom Teaching

Interactive Teaching Algorithms for Inverse Reinforcement Learning

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

Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch

Interactive Teaching Algorithms for Inverse Reinforcement Learning

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics