Glen Berseth

Biography

Glen Berseth is an assistant professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal and a core academic member of Mila – Quebec Artificial Intelligence Institute.

He is a Canada CIFAR AI Chair and co-directs the Robotics and Embodied AI Lab (REAL). He was formerly a postdoctoral researcher at Berkeley Artificial Intelligence Research (BAIR), working with Sergey Levine.

Berseth’s previous and current research has focused on solving sequential decision-making problems (planning) for real-world autonomous learning systems (robots). More specifically, his research has focused on human-robot collaboration, reinforcement, and continual-, meta-, multi-agent and hierarchical learning.

He has published in the top venues in robotics, machine learning and computer animation. He teaches a course on robot learning at Université de Montréal and at Mila, in which he covers the most recent research on machine learning techniques for creating generalist robots.

Current Students

Özgür Aslan

PhD - Université de Montréal

PhD - Université de Montréal

Master's Research - Université de Montréal

Website

Roger Creus-Castanyer

PhD - Université de Montréal

Co-supervisor :

PhD - McGill University

Principal supervisor :

Hsiu-Chin Lin

Léa Demeule

PhD - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Principal supervisor :

Liam Paull

Kumaraditya Gupta

PhD

Principal supervisor :

Collaborating researcher - Université de Montréal

Adriana Knatchbull-Hugessen

PhD - Université de Montréal

Artur Kuramshin

Master's Research - Université de Montréal

Website

Daniel Lawson

PhD - Université de Montréal

Co-supervisor :

Postdoctorate - Université de Montréal

Co-supervisor :

Master's Research - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Co-supervisor :

Marc Gendron-Bellemare

Collaborating researcher

Real-time Reinforcement Learning

Siddarth Venkatraman

PhD - Université de Montréal

Co-supervisor :

Albert Zhan

PhD - Université de Montréal

Blog Posts

Deux robots dans une cuisine, en train de préparer le dîner. L'un coupe les légumes et l'autre fait une omelette.

June 20, 2025

Ivan Anokhin

Matthew Riemer

Rishav Rishav

Gopeshh Subbaraj

Glen Berseth

Read the article

February 15, 2022

Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation Primary tabs View Edit(active tab) Delete Revisions

Jędrzej Orbik

Charles Sun

Coline Devin

Glen Berseth

Read the article

Publications

What Matters for Maximizing Data Reuse In Value-based Deep Reinforcement Learning

Roger Creus Castanyer

Pablo Samuel Castro

A key ingredient for successfully applying deep reinforcement learning to challenging tasks is the effective use of data at scale. Although … (see more)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.

2025-06-30

rl-conference.cc/RLC/2025/Workshop/Finding_the_Frame (published)

Zero-Shot Constraint Satisfaction with Forward- Backward Representations

Adriana Hugessen

Harley Wiltzer

Cyrus Neary

Amy Zhang

Traditionally, constrained policy optimization with Reinforcement Learning (RL) requires learning a new policy from scratch for any new envi… (see more)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

2025-06-30

rl-conference.cc/RLC/2025/Workshop/RLBrew (published)

Training PPO-Clip with Parallelized Data Generation: A Case of Fixed-Point Convergence

Homayoun Honari

Roger Creus Castanyer

Pablo Samuel Castro

In recent years, with the increase in the compute power of GPUs, parallelized data collection has become the dominant approach for training … (see more)reinforcement learning (RL) agents. Proximal Policy Optimization (PPO) is one of the widely-used on-policy methods for training RL agents. In this paper, we focus on the training behavior of PPO-Clip with the increase in the number of parallel environments. In particular, we show that as we increase the amount of data used to train PPO-Clip, the optimized policy would converge to a fixed distribution. We use the results to study the behavior of PPO-Clip in two case studies: the effect of change in the minibatch size and the effect of increase in the number of parallel environments versus the increase in the rollout lengths. The experiments show that settings with high-return PPO runs result in slower convergence to the fixed-distribution and higher consecutive KL divergence changes. Our results aim to offer a better understanding for the prediction of the performance of PPO with the scaling of the parallel environments.

2025-06-21

rl-conference.cc/RLC/2025/Workshop/IBRL (published)

Scalable Tree Search over Graphs with Learned Action Pruning for Power Grid Control

Florence Cloutier

Cyrus Neary

Adriana Hugessen

Viktor Todosijevic

Zina Kamel

As real-world infrastructure systems become increasingly complex and large-scale, there is a growing need for learning-based control strateg… (see more)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.

2025-06-16

rl-conference.cc/RLC/2025/Workshop/RL4RS (published)

Exploration by Exploitation: Curriculum Learning for Reinforcement Learning Agents through Competence-Based Curriculum Policy Search

Tabitha Edith Lee

Nan Rosemary Ke

Sarvesh Patil

Annya Dahmani

Eunice Yiu

Esra'a Saleh

Alison Gopnik

Oliver Kroemer

2025-06-11

ICML.cc/2025/Workshop/EXAIT (poster)

Efficient Morphology-Aware Policy Transfer to New Embodiments

Michael Przystupa

Hongyao Tang

Mariano Phielipp

Santiago Miret

Martin Jagersand

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… (see more)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.

2025-05-08

rl-conference.cc/RLC/2025/Conference (published)

Outsourced Diffusion Sampling: Efficient Posterior Inference in Latent Spaces of Generative Models

Any well-behaved generative model over a variable …

2025-04-30

International Conference on Machine Learning (poster)

proceedings.mlr.press

RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning

Mingqi Yuan

Roger Creus Castanyer

Bin Li

Xin Jin

Wenjun Zeng

2025-04-23

TMLR (accepted)

Laurence Perreault-Levasseur

Solving Bayesian Inverse Problems with Diffusion Priors and Off-Policy RL

Yoshua Bengio

Nikolay Malkin

This paper presents a practical application of Relative Trajectory Balance (RTB), a recently introduced off-policy reinforcement learning (R… (see more)L) objective that can asymptotically solve Bayesian inverse problems optimally. We extend the original work by using RTB to train conditional diffusion model posteriors from pretrained unconditional priors for challenging linear and non-linear inverse problems in vision, and science. We use the objective alongside techniques such as off-policy backtracking exploration to improve training. Importantly, our results show that existing training-free diffusion posterior methods struggle to perform effective posterior inference in latent space due to inherent biases.

2025-03-05

ICLR.cc/2025/Workshop/DeLTa (poster)

Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference

Matthew D Riemer

Gopeshh Subbaraj

Irina Rish

Realtime environments change even as agents perform action inference and learning, thus requiring high interaction frequencies to effectivel… (see more)y minimize regret. However, recent advances in machine learning involve larger neural networks with longer inference times, raising questions about their applicability in realtime systems where reaction time is crucial. We present an analysis of lower bounds on regret in realtime reinforcement learning (RL) environments to show that minimizing long-term regret is generally impossible within the typical sequential interaction and learning paradigm, but often becomes possible when sufficient asynchronous compute is available. We propose novel algorithms for staggering asynchronous inference processes to ensure that actions are taken at consistent time intervals, and demonstrate that use of models with high action inference times is only constrained by the environment's effective stochasticity over the inference horizon, and not by action frequency. Our analysis shows that the number of inference processes needed scales linearly with increasing inference times while enabling use of models that are multiple orders of magnitude larger than existing approaches when learning from a realtime simulation of Game Boy games such as Pokémon and Tetris.

2025-01-21

ICLR.cc/2025/Conference (poster)

Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching

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.

2025-01-21

ICLR.cc/2025/Conference (poster)