3 papers
cs.AI2026
Context-Sensitive Abstractions for Reinforcement Learning with Parameterized Actions
Rashmeet Kaur Nayyar, Naman Shah, Siddharth Srivastava
Real-world sequential decision-making often involves parameterized action spaces that require both, decisions regarding discrete actions and decisions about continuous action param…
cs.RO2025
Using Explainable AI and Hierarchical Planning for Outreach with Robots
Rushang Karia, Jayesh Nagpal, Daksh Dobhal +4
Understanding how robots plan and execute tasks is crucial in today's world, where they are becoming more prevalent in our daily lives. However, teaching non-experts, such as K-12…
cs.AI2024
Autonomous Option Invention for Continual Hierarchical Reinforcement Learning and Planning
Rashmeet Kaur Nayyar, Siddharth Srivastava
Abstraction is key to scaling up reinforcement learning (RL). However, autonomously learning abstract state and action representations to enable transfer and generalization remains…