53 citations · 133 across the 17 of their papers we have counts for
5 papers · 1 filter
FP-IRL: Fokker--Planck Inverse Reinforcement Learning -- A Physics-Constrained Approach to Markov Decision Processes
Chengyang Huang, Siddhartha Srivastava, Kenneth K. Y. Ho +4
Inverse reinforcement learning (IRL) is a powerful paradigm for uncovering the incentive structure that drives agent behavior, by inferring an unknown reward function from observed…
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…
Multi-Task Option Learning and Discovery for Stochastic Path Planning
Naman Shah, Siddharth Srivastava
This paper addresses the problem of reliably and efficiently solving broad classes of long-horizon stochastic path planning problems. Starting with a vanilla RL formulation with a…
Relational Abstractions for Generalized Reinforcement Learning on Symbolic Problems
Rushang Karia, Siddharth Srivastava
Reinforcement learning in problems with symbolic state spaces is challenging due to the need for reasoning over long horizons. This paper presents a new approach that utilizes rela…
Learning Generalized Relational Heuristic Networks for Model-Agnostic Planning
Rushang Karia, Siddharth Srivastava
Computing goal-directed behavior is essential to designing efficient AI systems. Due to the computational complexity of planning, current approaches rely primarily upon hand-coded…