Events Calendar

PhD Thesis Proposal - Anand Balakrishnan

Title: From Qualitative to Quantitative Objectives for Neurosymbolic Control and Planning
 
Date: Dec 17, 2024  
 
Time: 12 PM - 1 PM  
 
Location: GCS 302C  
 
Committee members: Jyotirmoy Deshmukh (chair), Rahul Jain, Lars Lindemann, Erdem Biyik  
 
Abstract: Reinforcement Learning (RL) is a popular paradigm by which an autonomous agent learns to perform complex tasks and behaviors through trial and error, facilitated by providing rewards to the agent. Effectively, these reward functions encode the high-level behavior intended by the designer, making the satisfactory performance of the tasks by the RL agent highly dependent on the reward functions. However, this raises concerns about safety and interpretability in the learned control policies. To this end, we propose using formal specification paradigms that can express complex behaviors unambiguously, including time-dependent tasks like sequential tasks and patrolling tasks.In this proposal, we first present how to extract quantitative rewards from such qualitative specifications without altering them and demonstrate through empirical and theoretical analysis the various guarantees and trade-offs associated with these techniques. We then derive novel representations for the specifications so that their structure can be directly exploited by optimization algorithms and propose how these representations can be leveraged to perform neurosymbolic control for complex systems.

Location: Ginsburg Hall (GCS) - 302C

Audiences: Everyone Is Invited

Contact: Ellecia Williams