Portrait of Arnav Kumar Jain is unavailable

Arnav Kumar Jain

PhD - Université de Montréal
Supervisor
Irina Rish
Research Topics
Reinforcement Learning
Website
Google Scholar
GitHub

Publications

Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching
Arnav Kumar Jain
Harley Wiltzer
Jesse Farebrother
Irina Rish
Glen Berseth
Sanjiban Choudhury
2025-01-22
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching
Arnav Kumar Jain
Harley Wiltzer
Jesse Farebrother
Irina Rish
Glen Berseth
Sanjiban Choudhury
In inverse reinforcement learning (IRL), an agent seeks to replicate expert demonstrations through interactions with the environment. Tradit… (see more)ionally, IRL is treated as an adversarial game, where an adversary searches over reward models, and a learner optimizes the reward through repeated RL procedures. This game-solving approach is both computationally expensive and difficult to stabilize. In this work, we propose a novel approach to IRL by direct policy optimization: exploiting a linear factorization of the return as the inner product of successor features and a reward vector, we design an IRL algorithm by policy gradient descent on the gap between the learner and expert features. Our non-adversarial method does not require learning a reward function and can be solved seamlessly with existing actor-critic RL algorithms. Remarkably, our approach works in state-only settings without expert action labels, a setting which behavior cloning (BC) cannot solve. Empirical results demonstrate that our method learns from as few as a single expert demonstration and achieves improved performance on various control tasks.
2024-11-11
ArXiv (preprint)
doi.org
arxiv.org
Revisiting Successor Features for Inverse Reinforcement Learning
Arnav Kumar Jain
Harley Wiltzer
Jesse Farebrother
Irina Rish
Glen Berseth
Sanjiban Choudhury
2024-06-17
ICML.cc/2024/Workshop/MFHAIA (poster)
openreview.net
openreview.net
Maximum State Entropy Exploration using Predecessor and Successor Representations
Arnav Kumar Jain
Lucas Lehnert
Irina Rish
Glen Berseth
Animals have a developed ability to explore that aids them in important tasks such as locating food, exploring for shelter, and finding misp… (see more)laced items. These exploration skills necessarily track where they have been so that they can plan for finding items with relative efficiency. Contemporary exploration algorithms often learn a less efficient exploration strategy because they either condition only on the current state or simply rely on making random open-loop exploratory moves. In this work, we propose
openreview.net