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Arnav Kumar Jain

arnav-kumar.jain@mila.quebec
Doctorat - UdeM
Superviseur⋅e principal⋅e
Irina Rish
Sujets de recherche
Apprentissage par renforcement
Site web
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
In inverse reinforcement learning (IRL), an agent seeks to replicate expert demonstrations through interactions with the environment. Tradit… (voir plus)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.
2025-01-21
International Conference on Learning Representations (poster)
doi.org
openreview.net
Revisiting Successor Features for Inverse Reinforcement Learning
Arnav Kumar Jain
Harley Wiltzer
Jesse Farebrother
Irina Rish
Glen Berseth
Sanjiban Choudhury
2024-06-16
MFHAIA @ International Conference on Machine Learning (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… (voir plus)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
2023-09-20
NeurIPS.cc/2023/Conference (poster)
doi.org
openreview.net
Learning Robust Dynamics through Variational Sparse Gating
Arnav Kumar Jain
Shivakanth Sujit
Shruti Joshi
Vincent Michalski
Danijar Hafner
Samira Ebrahimi-Kahou
Learning world models from their sensory inputs enables agents to plan for actions by imagining their future outcomes. World models have pre… (voir plus)viously been shown to improve sample-efficiency in simulated environments with few objects, but have not yet been applied successfully to environments with many objects. In environments with many objects, often only a small number of them are moving or interacting at the same time. In this paper, we investigate integrating this inductive bias of sparse interactions into the latent dynamics of world models trained from pixels. First, we introduce Variational Sparse Gating (VSG), a latent dynamics model that updates its feature dimensions sparsely through stochastic binary gates. Moreover, we propose a simplified architecture Simple Variational Sparse Gating (SVSG) that removes the deterministic pathway of previous models, resulting in a fully stochastic transition function that leverages the VSG mechanism. We evaluate the two model architectures in the BringBackShapes (BBS) environment that features a large number of moving objects and partial observability, demonstrating clear improvements over prior models.
2022-10-30
Neural Information Processing Systems (accepté)
doi.org
openreview.net