PhD Thesis Proposal - Ayush Jain

Thesis Proposal: Ayush Jain
Date: February 5, 2024 (Monday), 4 pm - 6 pm
Location: TBD
 
Committee: Erdem Biyik, Joseph J Lim, Gaurav Sukhatme, Stefanos Nikolaidis, Fefei Qian
 
Title: Enabling Robust Reinforcement Learning in Challenging Action Spaces
 
Abstract: The action space of an agent defines its interface to interact with the world. It can take two forms: discrete, as in recommender systems making decisions from millions of choices, or continuous, as in robots actuating control movements. While humans excel at a vast range of action spaces, from deciding between potentially unseen choices to making precise dexterous control like in surgery, conventional reinforcement learning (RL) is limited to simple action spaces beyond which agents fail entirely. Concretely, discrete RL typically assumes a "static" action space that never changes, while continuous RL assumes a "smooth" action space such that nearby actions have similar consequences. My goal is to alleviate these assumptions to broaden the applicability of RL agents to tasks with challenging action spaces. Thus, I build discrete RL algorithms that can adapt to any available action set and even choose from actions never seen before, such as recommending new items and choosing from unseen toolsets. In continuous action space tasks like robotics, I show how conventional agents get stuck on suboptimal actions due to a challenging action space. To address this, I propose a novel actor-critic algorithm enabling actors to search for more optimal actions.
 

Location: TBD

Audiences: Everyone Is Invited

Contact: CS Events