Title: Teachability and Interpretability in Reinforcement Learning

Abstract:
There have been many recent advancements in the field of reinforcement learning, starting from the Deep Q Network playing various Atari 2600 games all the way to Google Deempind’s Alphastar playing competitively in the game StarCraft. However, as the field challenges more complex environments, the current methods of training models and understanding their decision making become less effective. Currently, the problem is partially dealt with by simply adding more resources, but the need for a better solution remains.

This thesis proposes a reinforcement learning framework where a teacher or entity with domain knowledge of the task to complete can assist the agent in finding a policy, while also providing human observers a more understandable format for how the agent formulates its overall policy to complete a given task. This framework is evaluated on the Seaquest environment within the OpenAI Gym framework.

Zoom Link: https://unl.zoom.us/j/95441674526