Shibhansh Dohare

I am a Ph.D. student in the RLAI lab at the University of Alberta, advised by Dr. Richard Sutton and Dr. Rupam Mahmood. I did my undergrad at IIT Kanpur, majoring in Computer Science and Engineering.

My long-term research goal is to understand the working of our minds. Specifically, to find the fundamental computational principles that give rise to the mind. In pursuit of this goal, I'm working on various aspects of continual learning, deep learning, and reinforcement learning.

Besides research, I love climbing, social dancing, trying new food, running, playing board games, and the mountains.

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My favorite papers are highlighted.

We show that popular deep RL algorithms, like PPO, do not scale with experience. Their performance gets worse over time. We look deeper into this problem and provide simple solutions to reduce performance degradation.

Provide the first direct demonstration of the loss of plasticity faced by deep learning methods in continual learning problems.

We propose a new algorithm, continual backpropagation, that fully maintains plasticity. Continual backpropagation re-initializes a small fraction of less-used units and performs a gradient descent step at each update.

We show that standard Deep RL algorithms fail when the input contains noisy features. Dynamic sparse training successfully filters through the noisy features and performs well.

We present Gamma-nets, a method for generalizing value function estimation over timescale.

A novel algorithm for abstractive text summarization based on Abstract Meaning Representation.

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