I am passionate about doing research in Machine Learning with a special focus on Lifelong Reinforcement
Learning. I am a keen observer of human cognitive and learning processes. My research interest is in
using RL to mimic human cognitive processes and build collaborative and communicative AI agents with
common-sense knowledge about our world and an ability to plan, imagine and adapt to changes
in the environment.
We present IlliniMet, a system to automatically detect metaphorical words. Our
model combines the strengths of the contextualized representation by the widely used
RoBERTa model and the rich linguistic information from external resources such as
Planning with Model-Free and Model-Based Reinforcement Learning
CS 498, IR
Model-based RL has a strong advantage of being sample efficient. Once the model and the
cost function are known, we can plan the optimal controls without further sampling. We explore various model based
planning methods and experiment on various Gym Environments.