Portrait of Khimya Khetarpal

Khimya Khetarpal

Affiliate Member
Research Scientist, Google DeepMind
Research Topics
Machine Learning Theory
Online Learning
Reinforcement Learning
Representation Learning

Biography

Khimya Khetarpal is a Research Scientist at Google Deepmind. She earned her PhD in Computer Science from the Reasoning and Learning Lab at McGill University and Mila, advised by Doina Precup. She is broadly interested in artificial intelligence and reinforcement learning. Her current research interests focus on how RL agents learn to efficiently represent the world's knowledge, plan with it, and adapt to changes over time. Khimya’s work has appeared in leading AI journals and conferences including NeurIPS, ICML, AAAI, AISTATS, ICLR, The Knowledge Engineering Review, ACM, JAIR and TMLR. Her work has also been featured in MIT Technology Review. She was recognized as a TMLR expert reviewer in 2023, one of the Rising Stars in EECS 2020, a finalist for Three Minute Thesis (3MT) competition in AAAI 2019, selected for the Doctoral Consortium at AAAI 2019, and awarded Best Paper Award (3rd Price) for an ICML 2018 workshop on lifelong learning. Throughout her career, she has sought to actively mentor through initiatives such as co-founding the Mila peer advising initiative, teaching and assisting AI4Good Lab, volunteering at Skype A Scientist, and mentoring at FIRST Robotics.

Her research aims to (1) understand intelligent behavior that bridges both action and perception grounded in theoretical foundations of reinforcement learning, and (2) build AI agents to efficiently represent the world's knowledge, plan with it, and adapt to changes over time through learning and interaction.

She currently approaches this with the following research directions:

- Selective Attention for Fast Adaptation and Robustness

- Learning Abstractions and Affordances

- Discovery and Continual Reinforcement Learning

Current Students

Lynn Cherif
Master's Research - McGill University
Principal supervisor :
Doina Precup
Website
Google Scholar
Daniel Lawson
PhD - Université de Montréal
Principal supervisor :
Glen Berseth
Website
Github
Google Scholar

Publications

Cracking the Code of Action: A Generative Approach to Affordances for Reinforcement Learning
Lynn Cherif
Flemming Kondrup
David Venuto
Ankit Anand
Doina Precup
Khimya Khetarpal
Agents that can autonomously navigate the web through a graphical user interface (GUI) using a unified action space (e.g., mouse and keyboar… (see more)d actions) can require very large amounts of domain-specific expert demonstrations to achieve good performance. Low sample efficiency is often exacerbated in sparse-reward and large-action-space environments, such as a web GUI, where only a few actions are relevant in any given situation. In this work, we consider the low-data regime, with limited or no access to expert behavior. To enable sample-efficient learning, we explore the effect of constraining the action space through intent-based affordances -- i.e., considering in any situation only the subset of actions that achieve a desired outcome. We propose **Code as Generative Affordances**
2025-03-05
ICLR.cc/2025/Workshop/DL4C (published)
openreview.net
openreview.net
Agency Is Frame-Dependent
David Abel
Andre Barreto
Michael Bowling
Will Dabney
Shi Dong
Steven Hansen
Anna Harutyunyan
Khimya Khetarpal
Clare Lyle
Razvan Pascanu
Georgios Piliouras
Doina Precup
Jonathan Richens
Mark Rowland
Tom Schaul
Satinder Singh
Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science… (see more), and artificial intelligence. Determining if a system exhibits agency is a notoriously difficult question: Dennett (1989), for instance, highlights the puzzle of determining which principles can decide whether a rock, a thermostat, or a robot each possess agency. We here address this puzzle from the viewpoint of reinforcement learning by arguing that agency is fundamentally frame-dependent: Any measurement of a system's agency must be made relative to a reference frame. We support this claim by presenting a philosophical argument that each of the essential properties of agency proposed by Barandiaran et al. (2009) and Moreno (2018) are themselves frame-dependent. We conclude that any basic science of agency requires frame-dependence, and discuss the implications of this claim for reinforcement learning.
2025-02-06
ArXiv (preprint)
doi.org
arxiv.org
Agency Is Frame-Dependent
David Abel
Andre Barreto
Michael Bowling
Will Dabney
Shi Dong
Steven Hansen
A. Harutyunyan
Khimya Khetarpal
Clare Lyle
Razvan Pascanu
Georgios Piliouras
Doina Precup
Jonathan Richens
Mark Rowland
Tom Schaul
Satinder Singh
Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science… (see more), and artificial intelligence. Determining if a system exhibits agency is a notoriously difficult question: Dennett (1989), for instance, highlights the puzzle of determining which principles can decide whether a rock, a thermostat, or a robot each possess agency. We here address this puzzle from the viewpoint of reinforcement learning by arguing that agency is fundamentally frame-dependent: Any measurement of a system's agency must be made relative to a reference frame. We support this claim by presenting a philosophical argument that each of the essential properties of agency proposed by Barandiaran et al. (2009) and Moreno (2018) are themselves frame-dependent. We conclude that any basic science of agency requires frame-dependence, and discuss the implications of this claim for reinforcement learning.
2025-02-06
ArXiv (preprint)
arxiv.org
arxiv.org
Optimizing Return Distributions with Distributional Dynamic Programming
Bernardo Avila Pires
Mark Rowland
Diana Borsa
Zhaohan Daniel Guo
Khimya Khetarpal
Andre Barreto
David Abel
Remi Munos
Will Dabney
We introduce distributional dynamic programming (DP) methods for optimizing statistical functionals of the return distribution, with standar… (see more)d reinforcement learning as a special case. Previous distributional DP methods could optimize the same class of expected utilities as classic DP. To go beyond expected utilities, we combine distributional DP with stock augmentation, a technique previously introduced for classic DP in the context of risk-sensitive RL, where the MDP state is augmented with a statistic of the rewards obtained so far (since the first time step). We find that a number of recently studied problems can be formulated as stock-augmented return distribution optimization, and we show that we can use distributional DP to solve them. We analyze distributional value and policy iteration, with bounds and a study of what objectives these distributional DP methods can or cannot optimize. We describe a number of applications outlining how to use distributional DP to solve different stock-augmented return distribution optimization problems, for example maximizing conditional value-at-risk, and homeostatic regulation. To highlight the practical potential of stock-augmented return distribution optimization and distributional DP, we combine the core ideas of distributional value iteration with the deep RL agent DQN, and empirically evaluate it for solving instances of the applications discussed.
2025-01-22
ArXiv (preprint)
arxiv.org
arxiv.org
Optimizing Return Distributions with Distributional Dynamic Programming
Bernardo Avila Pires
Mark Rowland
Diana Borsa
Zhaohan Daniel Guo
Khimya Khetarpal
Andre Barreto
David Abel
Remi Munos
Will Dabney
We introduce distributional dynamic programming (DP) methods for optimizing statistical functionals of the return distribution, with standar… (see more)d reinforcement learning as a special case. Previous distributional DP methods could optimize the same class of expected utilities as classic DP. To go beyond expected utilities, we combine distributional DP with stock augmentation, a technique previously introduced for classic DP in the context of risk-sensitive RL, where the MDP state is augmented with a statistic of the rewards obtained so far (since the first time step). We find that a number of recently studied problems can be formulated as stock-augmented return distribution optimization, and we show that we can use distributional DP to solve them. We analyze distributional value and policy iteration, with bounds and a study of what objectives these distributional DP methods can or cannot optimize. We describe a number of applications outlining how to use distributional DP to solve different stock-augmented return distribution optimization problems, for example maximizing conditional value-at-risk, and homeostatic regulation. To highlight the practical potential of stock-augmented return distribution optimization and distributional DP, we combine the core ideas of distributional value iteration with the deep RL agent DQN, and empirically evaluate it for solving instances of the applications discussed.
2025-01-22
ArXiv (preprint)
doi.org
arxiv.org
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning
Khimya Khetarpal
Zhaohan Daniel Guo
Bernardo Avila Pires
Yunhao Tang
Clare Lyle
Mark Rowland
Nicolas Heess
Diana Borsa
Arthur Guez
Will Dabney
2025-01-22
aistats.org/AISTATS/2025/Conference (poster)
doi.org
openreview.net
Balancing Context Length and Mixing Times for Reinforcement Learning at Scale
Matthew D Riemer
Khimya Khetarpal
Janarthanan Rajendran
Mila Janarthanan
Sarath Chandar
É. Montréal
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
Normalization and effective learning rates in reinforcement learning
Clare Lyle
Zeyu Zheng
Khimya Khetarpal
James Martens
Hado van Hasselt
Razvan Pascanu
Will Dabney
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
Normalization and effective learning rates in reinforcement learning
Clare Lyle
Zeyu Zheng
Khimya Khetarpal
James Martens
Hado van Hasselt
Razvan Pascanu
Will Dabney
Normalization layers have recently experienced a renaissance in the deep reinforcement learning and continual learning literature, with seve… (see more)ral works highlighting diverse benefits such as improving loss landscape conditioning and combatting overestimation bias. However, normalization brings with it a subtle but important side effect: an equivalence between growth in the norm of the network parameters and decay in the effective learning rate. This becomes problematic in continual learning settings, where the resulting effective learning rate schedule may decay to near zero too quickly relative to the timescale of the learning problem. We propose to make the learning rate schedule explicit with a simple re-parameterization which we call Normalize-and-Project (NaP), which couples the insertion of normalization layers with weight projection, ensuring that the effective learning rate remains constant throughout training. This technique reveals itself as a powerful analytical tool to better understand learning rate schedules in deep reinforcement learning, and as a means of improving robustness to nonstationarity in synthetic plasticity loss benchmarks along with both the single-task and sequential variants of the Arcade Learning Environment. We also show that our approach can be easily applied to popular architectures such as ResNets and transformers while recovering and in some cases even slightly improving the performance of the base model in common stationary benchmarks.
2024-07-01
ArXiv (preprint)
doi.org
arxiv.org
Normalization and effective learning rates in reinforcement learning
Clare Lyle
Zeyu Zheng
Khimya Khetarpal
James Martens
Hado van Hasselt
Razvan Pascanu
Will Dabney
Normalization layers have recently experienced a renaissance in the deep reinforcement learning and continual learning literature, with seve… (see more)ral works highlighting diverse benefits such as improving loss landscape conditioning and combatting overestimation bias. However, normalization brings with it a subtle but important side effect: an equivalence between growth in the norm of the network parameters and decay in the effective learning rate. This becomes problematic in continual learning settings, where the resulting effective learning rate schedule may decay to near zero too quickly relative to the timescale of the learning problem. We propose to make the learning rate schedule explicit with a simple re-parameterization which we call Normalize-and-Project (NaP), which couples the insertion of normalization layers with weight projection, ensuring that the effective learning rate remains constant throughout training. This technique reveals itself as a powerful analytical tool to better understand learning rate schedules in deep reinforcement learning, and as a means of improving robustness to nonstationarity in synthetic plasticity loss benchmarks along with both the single-task and sequential variants of the Arcade Learning Environment. We also show that our approach can be easily applied to popular architectures such as ResNets and transformers while recovering and in some cases even slightly improving the performance of the base model in common stationary benchmarks.
2024-07-01
ArXiv (preprint)
doi.org
arxiv.org
Disentangling the Causes of Plasticity Loss in Neural Networks
Clare Lyle
Zeyu Zheng
Khimya Khetarpal
Hado van Hasselt
Razvan Pascanu
James Martens
Will Dabney
Underpinning the past decades of work on the design, initialization, and optimization of neural networks is a seemingly innocuous assumption… (see more): that the network is trained on a \textit{stationary} data distribution. In settings where this assumption is violated, e.g.\ deep reinforcement learning, learning algorithms become unstable and brittle with respect to hyperparameters and even random seeds. One factor driving this instability is the loss of plasticity, meaning that updating the network's predictions in response to new information becomes more difficult as training progresses. While many recent works provide analyses and partial solutions to this phenomenon, a fundamental question remains unanswered: to what extent do known mechanisms of plasticity loss overlap, and how can mitigation strategies be combined to best maintain the trainability of a network? This paper addresses these questions, showing that loss of plasticity can be decomposed into multiple independent mechanisms and that, while intervening on any single mechanism is insufficient to avoid the loss of plasticity in all cases, intervening on multiple mechanisms in conjunction results in highly robust learning algorithms. We show that a combination of layer normalization and weight decay is highly effective at maintaining plasticity in a variety of synthetic nonstationary learning tasks, and further demonstrate its effectiveness on naturally arising nonstationarities, including reinforcement learning in the Arcade Learning Environment.
2024-02-29
ArXiv (preprint)
doi.org
arxiv.org
Disentangling the Causes of Plasticity Loss in Neural Networks
Clare Lyle
Zeyu Zheng
Khimya Khetarpal
Hado van Hasselt
Razvan Pascanu
James Martens
Will Dabney
Underpinning the past decades of work on the design, initialization, and optimization of neural networks is a seemingly innocuous assumption… (see more): that the network is trained on a \textit{stationary} data distribution. In settings where this assumption is violated, e.g.\ deep reinforcement learning, learning algorithms become unstable and brittle with respect to hyperparameters and even random seeds. One factor driving this instability is the loss of plasticity, meaning that updating the network's predictions in response to new information becomes more difficult as training progresses. While many recent works provide analyses and partial solutions to this phenomenon, a fundamental question remains unanswered: to what extent do known mechanisms of plasticity loss overlap, and how can mitigation strategies be combined to best maintain the trainability of a network? This paper addresses these questions, showing that loss of plasticity can be decomposed into multiple independent mechanisms and that, while intervening on any single mechanism is insufficient to avoid the loss of plasticity in all cases, intervening on multiple mechanisms in conjunction results in highly robust learning algorithms. We show that a combination of layer normalization and weight decay is highly effective at maintaining plasticity in a variety of synthetic nonstationary learning tasks, and further demonstrate its effectiveness on naturally arising nonstationarities, including reinforcement learning in the Arcade Learning Environment.
2024-02-29
ArXiv (preprint)
doi.org
arxiv.org