Portrait de Glen Berseth

Glen Berseth

Membre académique principal
Chaire en IA Canada-CIFAR
Professeur agrégé, Université de Montréal, Département d'informatique et de recherche opérationnelle
Sujets de recherche
Apprentissage par renforcement
Apprentissage profond
Robotique

Biographie

Glen Berseth est professeur agrégé au Département d'informatique et de recherche opérationnelle (DIRO) de l'Université de Montréal, membre académique principal de Mila – Institut québécois d'intelligence artificielle, détenteur d’une chaire en IA Canada-CIFAR et codirecteur du Laboratoire de robotique et d’IA intégrative de Montréal (REAL). Il a été chercheur postdoctoral à Berkeley Artificial Intelligence Research (BAIR), où il a travaillé avec Sergey Levine. Ses recherches portent sur la résolution de problèmes de prise de décision séquentielle (planification) pour les systèmes d'apprentissage autonomes du monde réel (robots). Elles ont couvert les domaines de la collaboration humain-robot, du renforcement, ainsi que de l'apprentissage continu, multiagent et hiérarchique et du méta-apprentissage. Glen Berseth a fait paraître des articles dans les meilleures publications des domaines de la robotique, de l'apprentissage automatique et de l'animation informatique. Il donne également un cours sur l'apprentissage des robots à l'Université de Montréal et à Mila, couvrant les recherches les plus récentes sur les techniques d'apprentissage automatique pour la création de robots généralistes.

Étudiants actuels

Maîtrise recherche - UdeM
Maîtrise recherche - UdeM
Collaborateur·rice de recherche - Waterloo
Doctorat - McGill
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche - UdeM
Maîtrise recherche - UdeM
Doctorat - UdeM
Co-superviseur⋅e :
Postdoctorat - UdeM
Co-superviseur⋅e :
Maîtrise recherche - UdeM
Postdoctorat - UdeM
Co-superviseur⋅e :
Maîtrise professionnelle - UdeM
Stagiaire de recherche - UdeM
Doctorat - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Co-superviseur⋅e :

Publications

Adaptive Resolution Residual Networks — Generalizing Across Resolutions Easily and Efficiently
Mahtab Sandhu
The majority of signal data captured in the real world uses numerous sensors with different resolutions. In practice, most deep learning arc… (voir plus)hitectures are fixed-resolution; they consider a single resolution at training and inference time. This is convenient to implement but fails to fully take advantage of the diverse signal data that exists. In contrast, other deep learning architectures are adaptive-resolution; they directly allow various resolutions to be processed at training and inference time. This provides computational adaptivity but either sacrifices robustness or compatibility with mainstream layers, which hinders their use. In this work, we introduce Adaptive Resolution Residual Networks (ARRNs) to surpass this tradeoff. We construct ARRNs from Laplacian residuals, which serve as generic adaptive-resolution adapters for fixed-resolution layers. We use smoothing filters within Laplacian residuals to linearly separate input signals over a series of resolution steps. We can thereby skip Laplacian residuals to cast high-resolution ARRNs into low-resolution ARRNs that are computationally cheaper yet numerically identical over low-resolution signals. We guarantee this result when Laplacian residuals are implemented with perfect smoothing kernels. We complement this novel component with Laplacian dropout, which randomly omits Laplacian residuals during training. This regularizes for robustness to a distribution of lower resolutions. This also regularizes for numerical errors that may occur when Laplacian residuals are implemented with approximate smoothing kernels. We provide a solid grounding for the advantageous properties of ARRNs through a theoretical analysis based on neural operators, and empirically show that ARRNs embrace the challenge posed by diverse resolutions with computational adaptivity, robustness, and compatibility with mainstream layers.
Curiosity-Driven Exploration via Temporal \\ Contrastive Learning
Catherine Ji
Benjamin Eysenbach
Exploration remains a key challenge in reinforcement learning (RL), especially in long-horizon tasks and environments with high-dimensional … (voir plus)observations. A common strategy for effective exploration is to promote state coverage or novelty, which often involves estimating the agent's state visitation distribution. In this paper, we propose \textbf{C}uriosity-Driven Exploration via \textbf{Te}mporal \textbf{C}ontrastive Learning (\methodName), an exploration method based on temporal contrastive learning that rewards agents for reaching states with unexpected futures. This incentivizes uncovering meaningful less-visited states. \methodName is simple and does not require explicit density or uncertainty estimation, while learning representations aligned with the RL objective. It consistently outperforms standard baselines in complex mazes using different embodiments (Ant and Humanoid) and robotic manipulation tasks, while also yielding more diverse behaviors in Craftax without requiring task-specific information.
Curiosity-Driven Exploration via Temporal Contrastive Learning
Catherine Ji
Benjamin Eysenbach
Effective exploration in reinforcement learning requires keeping track not just of where the agent has been, but also of how the agent think… (voir plus)s about and represents the world: an agent should explore states that enable it to learn powerful representations. Temporal representations can include the information required to solve any potential task while avoiding the computational cost of reconstruction. In this paper, we propose an exploration method that uses temporal contrastive representations to drive exploration, maximizing coverage as seen through the lens of these temporal representations. We demonstrate complex exploration behaviors in locomotion, manipulation, and embodied-AI tasks, revealing previously unknown capabilities and behaviors once achievable only via extrinsic rewards.
Is Exploration or Optimization the Problem for Deep Reinforcement Learning?
In the era of deep reinforcement learning, making progress is more complex, as the collected experience must be compressed into a deep model… (voir plus) for future exploitation and sampling. Many papers have shown that training a deep learning policy under the changing state and action distribution leads to sub-optimal performance even collapse. This naturally leads to the concern that even if the community creates improved exploration algorithms or reward objectives, will those improvements fall on the \textit{deaf ears} of optimization difficulties. This work proposes a new \textit{pracitcal} sub-optimality estimator to determine optimization limitations of deep reinforcement learning algorithms. Through experiments acrossenvironments and RL algorithms, it is shown that the difference between the best data generated is
Self-Predictive Representations for Combinatorial Generalization in Behavioral Cloning
Behavioral cloning (BC) methods trained with supervised learning (SL) are an effective way to learn policies from human demonstrations in do… (voir plus)mains like robotics. Goal-conditioning these policies enables a single generalist policy to capture diverse behaviors contained within an offline dataset. While goal-conditioned behavior cloning (GCBC) methods can perform well on in-distribution training tasks, they do not necessarily generalize zero-shot to tasks that require conditioning on novel state-goal pairs, i.e. combinatorial generalization. In part, this limitation can be attributed to a lack of temporal consistency in the state representation learned by BC; if temporally related states are encoded to similar latent representations, then the out-of-distribution gap for novel state-goal pairs would be reduced. Hence, encouraging this temporal consistency in the representation space should facilitate combinatorial generalization. Successor representations, which encode the distribution of future states visited from the current state, nicely encapsulate this property. However, previous methods for learning successor representations have relied on contrastive samples, temporal-difference (TD) learning, or both. In this work, we propose a simple yet effective representation learning objective,
What Matters for Maximizing Data Reuse In Value-based Deep Reinforcement Learning
Roger Creus Castanyer
A key ingredient for successfully applying deep reinforcement learning to challenging tasks is the effective use of data at scale. Although … (voir plus)originally deep RL algorithms achieved this by storing past experiences collected from a synchronous actor in an external replay memory [DQN; Mnih et al., 2013], follow-up works scaled training by collecting data asynchronously through distributed actors [R2D2; Kapturowski et al., 2018], and more recently by GPU-optimized parallelization [PQN; Gallici et al., 2024]. We argue that DQN, PQN, and R2D2 constitute a group of value-based methods for parallel training and study them to shed light on the dynamics induced by varying data collection schemes. We conduct a thorough empirical study to better understand these dynamics, and propose the Data Replay Ratio as a novel metric for quantifying data reuse. Our findings suggest that maximizing data reuse involves directly addressing the deadly triad: Q-lambda rollouts for reducing the bias from bootstrapping, the use of LayerNorm for stabilizing function approximation, and parallelized data collection for mitigating off-policy divergence.
Zero-Shot Constraint Satisfaction with Forward- Backward Representations
Adriana Hugessen
Cyrus Neary
Amy Zhang
Traditionally, constrained policy optimization with Reinforcement Learning (RL) requires learning a new policy from scratch for any new envi… (voir plus)ronment, goal or cost function, with limited generalization to new tasks and constraints. Given the sample inefficiency of many common deep RL methods, this procedure can be impractical for many real-world scenarios, particularly when constraints or tasks are changing. As an alternative, in the unconstrained setting, various works have sought to pre-train representations from offline datasets to accelerate policy optimization upon specification of a reward. Such methods can permit faster adaptation to new tasks in a given environment, dramatically improving sample efficiency. Recently, zero-shot policy optimization has been explored by leveraging a particular
Scalable Tree Search over Graphs with Learned Action Pruning for Power Grid Control
Cyrus Neary
Adriana Hugessen
Viktor Todosijević
As real-world infrastructure systems become increasingly complex and large-scale, there is a growing need for learning-based control strateg… (voir plus)ies that can make informed decisions in complex and dynamic environments. However, large-scale problems — such as power grid control — introduce high-dimensional action spaces and necessitate transferability across varying grid topologies. We introduce **H**ierarchical **E**xpert-Guided **R**econfiguration **O**ptimization for **G**raph **T**opologies, **HERO-GT**, a model-based planning approach that combines a pretrained graph neural network (GNN) for topology-aware action pruning with a Monte Carlo Tree Search (MCTS) planner for targeted, structured exploration. More specifically, the high-level GNN predicts a promising subset of actions, which the low-level MCTS agent uses to focus its search and reduce computational overhead while remaining adaptable to unseen graph structures. Furthermore, the MCTS planner leverages a given *default policy*---which may be defined, for example, by heuristics, problem relaxations, or rule-based methods---to bias the search and prioritize actions that are expected to improve performance over the default. We deploy HERO-GT in power grid environments, demonstrating that it not only improves over a strong default policy, but also scales to a realistic operational setting where exhaustive search becomes computationally infeasible.
Exploration by Exploitation: Curriculum Learning for Reinforcement Learning Agents through Competence-Based Curriculum Policy Search
Nan Rosemary Ke
Sarvesh Patil
Annya Dahmani
Eunice Yiu
Alison Gopnik
Oliver Kroemer
Self-Predictive Representations for Combinatorial Generalization in Behavioral Cloning
Behavioral cloning (BC) methods trained with supervised learning (SL) are an effective way to learn policies from human demonstrations in do… (voir plus)mains like robotics. Goal-conditioning these policies enables a single generalist policy to capture diverse behaviors contained within an offline dataset. While goal-conditioned behavior cloning (GCBC) methods can perform well on in-distribution training tasks, they do not necessarily generalize zero-shot to tasks that require conditioning on novel state-goal pairs, i.e. combinatorial generalization. In part, this limitation can be attributed to a lack of temporal consistency in the state representation learned by BC; if temporally related states are encoded to similar latent representations, then the out-of-distribution gap for novel state-goal pairs would be reduced. Hence, encouraging this temporal consistency in the representation space should facilitate combinatorial generalization. Successor representations, which encode the distribution of future states visited from the current state, nicely encapsulate this property. However, previous methods for learning successor representations have relied on contrastive samples, temporal-difference (TD) learning, or both. In this work, we propose a simple yet effective representation learning objective,
Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning
Scaling deep reinforcement learning networks is challenging and often results in degraded performance, yet the root causes of this failure m… (voir plus)ode remain poorly understood. Several recent works have proposed mechanisms to address this, but they are often complex and fail to highlight the causes underlying this difficulty. In this work, we conduct a series of empirical analyses which suggest that the combination of non-stationarity with gradient pathologies, due to suboptimal architectural choices, underlie the challenges of scale. We propose a series of direct interventions that stabilize gradient flow, enabling robust performance across a range of network depths and widths. Our interventions are simple to implement and compatible with well-established algorithms, and result in an effective mechanism that enables strong performance even at large scales. We validate our findings on a variety of agents and suites of environments.
Efficient Morphology-Aware Policy Transfer to New Embodiments
Hongyao Tang
Mariano Phielipp
Santiago Miret
Martin Jägersand
Matthew E. Taylor
Morphology-aware policy learning is a means of enhancing policy sample efficiency by aggregating data from multiple agents. These types of p… (voir plus)olicies have previously been shown to help generalize over dynamic, kinematic, and limb configuration variations between agent morphologies. Unfortunately, these policies still have sub-optimal zero-shot performance compared to end-to-end finetuning on morphologies at deployment. This limitation has ramifications in practical applications such as robotics because further data collection to perform end-to-end finetuning can be computationally expensive. In this work, we investigate combining morphology-aware pretraining with \textit{parameter efficient finetuning} (PEFT) techniques to help reduce the learnable parameters necessary to specialize a morphology-aware policy to a target embodiment. We compare directly tuning sub-sets of model weights, input learnable adapters, and prefix tuning techniques for online finetuning. Our analysis reveals that PEFT techniques in conjunction with policy pre-training generally help reduce the number of samples to necessary to improve a policy compared to training models end-to-end from scratch. We further find that tuning as few as less than 1\% of total parameters will improve policy performance compared the zero-shot performance of the base pretrained a policy.