MS Defense: Improved Reinforcement Learning, 3pm Sept 8

Reinforcement Learning (RL) has shown great promise in solving complex decision-making problems. However, the high sample complexity of RL algorithms has motivated researchers to explore alternative approaches that can reduce the amount of data needed for learning. One such approach is to incorporate expert knowledge through supervised learning from human experts. This thesis explores using representation learning with Siamese networks to extract information from limited expert traces effectively. Experiments in the CartPole environment show the effectiveness of this approach. Both the accuracy of the classifiers at choosing the optimal action (label) is measured, as well as their ability to accumulate reward in the domain.

Thesis Committee: Dr. Tim  Oates (chair), Dr. Cynthia Matuszek, Dr. Don Engel