3 citations · 3 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Learning Dynamic Abstract Representations for Sample-Efficient Reinforcement Learning
Mehdi Dadvar, Rashmeet Kaur Nayyar, Siddharth Srivastava
In many real-world problems, the learning agent needs to learn a problem's abstractions and solution simultaneously. However, most such abstractions need to be designed and refined…
Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems
Rushang Karia, Rashmeet Kaur Nayyar, Siddharth Srivastava
Several goal-oriented problems in the real-world can be naturally expressed as Stochastic Shortest Path Problems (SSPs). However, the computational complexity of solving SSPs makes…
Differential Assessment of Black-Box AI Agents
Rashmeet Kaur Nayyar, Pulkit Verma, Siddharth Srivastava
Much of the research on learning symbolic models of AI agents focuses on agents with stationary models. This assumption fails to hold in settings where the agent's capabilities may…