Portrait of Glen Berseth

Glen Berseth

Core Academic Member
Canada CIFAR AI Chair
Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Topics
Deep Learning
Reinforcement Learning

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

PhD - Université de Montréal
Master's Research - Université de Montréal
Professional Master's - Université de Montréal
PhD - Université de Montréal
Co-supervisor :
PhD - McGill University
Principal supervisor :
PhD - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
Principal supervisor :
Collaborating researcher
Principal supervisor :
Université de Montréal
PhD - Université de Montréal
Master's Research - Université de Montréal
PhD - Université de Montréal
Co-supervisor :
Postdoctorate - Université de Montréal
Co-supervisor :
Master's Research - Université de Montréal
Postdoctorate - Université de Montréal
Co-supervisor :
Professional Master's - Université de Montréal
Research Intern - Université de Montréal
PhD - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
Co-supervisor :

Publications

Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference
Matthew D Riemer
Gopeshh Subbaraj
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.
Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching
Arnav Kumar Jain
Harley Wiltzer
Jesse Farebrother
Sanjiban Choudhury
Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference
Matthew Riemer
Gopeshh Raaj Subbaraj
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 Pok\'emon and Tetris.
Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference
Matthew Riemer
Gopeshh Raaj Subbaraj
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 Pok\'emon and Tetris.
Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching
Arnav Kumar Jain
Harley Wiltzer
Jesse Farebrother
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.
Minimally Invasive Morphology Adaptation via Parameter Efficient Fine-Tuning
Michael Przystupa
Hongyao Tang
Mariano Phielipp
Santiago Miret
Martin Jägersand
Learning reinforcement learning policies to control individual robots is often computationally non-economical because minor variations in ro… (see more)bot morphology (e.g. dynamics or number of limbs) can negatively impact policy performance. This limitation has motivated morphology agnostic policy learning, in which a monolithic deep learning policy learns to generalize between robotic morphologies. Unfortunately, these policies still have sub-optimal zero-shot performance compared to end-to-end finetuning on target morphologies. This limitation has ramifications in practical robotic applications, as online finetuning large neural networks can require immense computation. In this work, we investigate \textit{parameter efficient finetuning} techniques to specialize morphology-agnostic policies to a target robot that minimizes the number of learnable parameters adapted during online learning. We compare direct finetuning, which update subsets of the base model parameters, and input-learnable approaches, which add additional parameters to manipulate inputs passed to the base model. Our analysis concludes that tuning relatively few parameters (0.01\% of the base model) can measurably improve policy performance over zero shot. These results serve a prescriptive purpose for future research for which scenarios certain PEFT approaches are best suited for adapting policy's to new robotic morphologies.
Learning Robust Representations for Transfer in Reinforcement Learning
Faisal Mohamed
Roger Creus Castanyer
Hongyao Tang
Zahra Sheikhbahaee
Learning transferable representations for deep reinforcement learning (RL) is a challenging problem due to the inherent non-stationarity, di… (see more)stribution shift, and unstable training dynamics. To be useful, a transferable representation needs to be robust to such factors. In this work, we introduce a new architecture and training strategy for learning robust representations for transfer learning in RL. We propose leveraging multiple CNN encoders and training them not to specialize in areas of the state space but instead to match each other's representation. We find that learned representations transfer well across many Atari tasks, resulting in better transfer learning performance and data efficiency than training from scratch.
Efficient Design-and-Control Automation with Reinforcement Learning and Adaptive Exploration
Jiajun Fan
Hongyao Tang
Michael Przystupa
Mariano Phielipp
Santiago Miret
Seeking good designs is a central goal of many important domains, such as robotics, integrated circuits (IC), medicine, and materials scienc… (see more)e. These design problems are expensive, time-consuming, and traditionally performed by human experts. Moreover, the barriers to domain knowledge make it challenging to propose a universal solution that generalizes to different design problems. In this paper, we propose a new method called Efficient Design and Stable Control (EDiSon) for automatic design and control in different design problems. The key ideas of our method are (1) interactive sequential modeling of the design and control process and (2) adaptive exploration and design replay. To decompose the difficulty of learning design and control as a whole, we leverage sequential modeling for both the design process and control process, with a design policy to generate step-by-step design proposals and a control policy to optimize the objective by operating the design. With deep reinforcement learning (RL), the policies learn to find good designs by maximizing a reward signal that evaluates the quality of designs. Furthermore, we propose an adaptive exploration and replay mechanism based on a design memory that maintains high-quality designs generated so far. By regulating between constructing a design from scratch or replaying a design from memory to refine it, EDiSon balances the trade-off between exploration and exploitation in the design space and stabilizes the learning of the control policy. In the experiments, we evaluate our method in robotic morphology design and Tetris-based design tasks. Our framework has the potential to significantly accelerate the discovery of optimized designs across diverse domains, including automated materials discovery, by improving the exploration in design space while ensuring efficiency.
Efficient Design-and-Control Automation with Reinforcement Learning and Adaptive Exploration
Jiajun Fan
Hongyao Tang
Michael Przystupa
Mariano Phielipp
Santiago Miret
Seeking good designs is a central goal of many important domains, such as robotics, integrated circuits (IC), medicine, and materials scienc… (see more)e. These design problems are expensive, time-consuming, and traditionally performed by human experts. Moreover, the barriers to domain knowledge make it challenging to propose a universal solution that generalizes to different design problems. In this paper, we propose a new method called Efficient Design and Stable Control (EDiSon) for automatic design and control in different design problems. The key ideas of our method are (1) interactive sequential modeling of the design and control process and (2) adaptive exploration and design replay. To decompose the difficulty of learning design and control as a whole, we leverage sequential modeling for both the design process and control process, with a design policy to generate step-by-step design proposals and a control policy to optimize the objective by operating the design. With deep reinforcement learning (RL), the policies learn to find good designs by maximizing a reward signal that evaluates the quality of designs. Furthermore, we propose an adaptive exploration and replay mechanism based on a design memory that maintains high-quality designs generated so far. By regulating between constructing a design from scratch or replaying a design from memory to refine it, EDiSon balances the trade-off between exploration and exploitation in the design space and stabilizes the learning of the control policy. In the experiments, we evaluate our method in robotic morphology design and Tetris-based design tasks. Our framework has the potential to significantly accelerate the discovery of optimized designs across diverse domains, including automated materials discovery, by improving the exploration in design space while ensuring efficiency.
Amortizing intractable inference in diffusion models for vision, language, and control
Siddarth Venkatraman
Moksh J. Jain
Luca Scimeca
Minsu Kim
Marcin Sendera
Mohsin Hasan
Luke Rowe
Sarthak Mittal
Pablo Lemos
Alexandre Adam
Jarrid Rector-Brooks
Nikolay Malkin
Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors … (see more)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,
Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn
Hongyao Tang
Deep neural networks provide Reinforcement Learning (RL) powerful function approximators to address large-scale decision-making problems. Ho… (see more)wever, these approximators introduce challenges due to the non-stationary nature of RL training. One source of the challenges in RL is that output predictions can churn, leading to uncontrolled changes after each batch update for states not included in the batch. Although such a churn phenomenon exists in each step of network training, how churn occurs and impacts RL remains under-explored. In this work, we start by characterizing churn in a view of Generalized Policy Iteration with function approximation, and we discover a chain effect of churn that leads to a cycle where the churns in value estimation and policy improvement compound and bias the learning dynamics throughout the iteration. Further, we concretize the study and focus on the learning issues caused by the chain effect in different settings, including greedy action deviation in value-based methods, trust region violation in proximal policy optimization, and dual bias of policy value in actor-critic methods. We then propose a method to reduce the chain effect across different settings, called Churn Approximated ReductIoN (CHAIN), which can be easily plugged into most existing DRL algorithms. Our experiments demonstrate the effectiveness of our method in both reducing churn and improving learning performance across online and offline, value-based and policy-based RL settings, as well as a scaling setting.
Simplifying Constraint Inference with Inverse Reinforcement Learning
Adriana Hugessen
Harley Wiltzer