Glen Berseth

Efficient Morphology-Aware Policy Transfer to New Embodiments

Michael Przystupa

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.

2025-05-09

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

openreview.net

Efficient Morphology-Aware Policy Transfer to New Embodiments

Michael Przystupa

Hongyao Tang

Glen Berseth

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.

2025-05-09

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

openreview.net

RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning

Mingqi Yuan

Roger Creus Castanyer

Bo Li

Xin Jin

Wenjun Zeng

Glen Berseth

2025-04-24

TMLR (accepté)

doi.org

openreview.net

Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models

Siddarth Venkatraman

Mohsin Hasan

Minsu Kim

Luca Scimeca

Marcin Sendera

Yoshua Bengio

Glen Berseth

Nikolay Malkin

Any well-behaved generative model over a variable …

2025-03-06

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

doi.org

openreview.net

Solving Bayesian inverse problems with diffusion priors and off-policy RL

Luca Scimeca

Siddarth Venkatraman

Moksh J. Jain

Minsu Kim

Marcin Sendera

Mohsin Hasan

Luke Rowe

Sarthak Mittal

Pablo Lemos

Emmanuel Bengio

Alexandre Adam

Jarrid Rector-Brooks

Yashar Hezaveh

Laurence Perreault-Levasseur

Yoshua Bengio

Glen Berseth

Nikolay Malkin

This paper presents a practical application of Relative Trajectory Balance (RTB), a recently introduced off-policy reinforcement learning (R… (voir plus)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.

2025-03-06

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

doi.org

openreview.net

Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models

Siddarth Venkatraman

Mohsin Hasan

Minsu Kim

Luca Scimeca

Marcin Sendera

Yoshua Bengio

Glen Berseth

Nikolay Malkin

Any well-behaved generative model over a variable …

2025-02-10

ArXiv (prépublication)

arxiv.org

Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference

Matthew D Riemer

Gopeshh Subbaraj

Glen Berseth

Irina Rish

Realtime environments change even as agents perform action inference and learning, thus requiring high interaction frequencies to effectivel… (voir plus)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.

2025-01-22

ICLR.cc/2025/Conference (poster)

doi.org

openreview.net

Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching

Arnav Kumar Jain

Harley Wiltzer

Jesse Farebrother

Irina Rish

Glen Berseth

Sanjiban Choudhury

2025-01-22

ICLR.cc/2025/Conference (poster)

doi.org

openreview.net

Towards Improving Exploration through Sibling Augmented GFlowNets

Kanika Madan

Alex Lamb

Emmanuel Bengio

Glen Berseth

Yoshua Bengio

Exploration is a key factor for the success of an active learning agent, especially when dealing with sparse extrinsic terminal rewards and … (voir plus)long trajectories. We introduce Sibling Augmented Generative Flow Networks (SA-GFN), a novel framework designed to enhance exploration and training efficiency of Generative Flow Networks (GFlowNets). SA-GFN uses a decoupled dual network architecture, comprising of a main Behavior Network and an exploratory Sibling Network, to enable a diverse exploration of the underlying distribution using intrinsic rewards. Inspired by the ideas on exploration from reinforcement learning, SA-GFN provides a general-purpose exploration and learning paradigm that integrates with multiple GFlowNet training objectives and is especially helpful for exploration over a wide range of sparse or low reward distributions and task structures. An extensive set of experiments across a diverse range of tasks, reward structures and trajectory lengths, along with a thorough set of ablations, demonstrate the superior performance of SA-GFN in terms of exploration efficacy and convergence speed as compared to the existing methods. In addition, SA-GFN's versatility and compatibility with different GFlowNet training objectives and intrinsic reward methods underscores its broad applicability in various problem domains.

2025-01-22

ICLR.cc/2025/Conference (poster)

openreview.net

Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control

Zhongyu Li

Xue Bin Peng

Pieter Abbeel

Sergey Levine

Glen Berseth

Koushil Sreenath

This paper presents a comprehensive study on using deep reinforcement learning (RL) to create dynamic locomotion controllers for bipedal rob… (voir plus)ots. Going beyond focusing on a single locomotion skill, we develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing. Our RL-based controller incorporates a novel dual-history architecture, utilizing both a long-term and short-term input/output (I/O) history of the robot. This control architecture, when trained through the proposed end-to-end RL approach, consistently outperforms other methods across a diverse range of skills in both simulation and the real world.The study also delves into the adaptivity and robustness introduced by the proposed RL system in developing locomotion controllers. We demonstrate that the proposed architecture can adapt to both time-invariant dynamics shifts and time-variant changes, such as contact events, by effectively using the robot's I/O history. Additionally, we identify task randomization as another key source of robustness, fostering better task generalization and compliance to disturbances. The resulting control policies can be successfully deployed on Cassie, a torque-controlled human-sized bipedal robot. This work pushes the limits of agility for bipedal robots through extensive real-world experiments. We demonstrate a diverse range of locomotion skills, including: robust standing, versatile walking, fast running with a demonstration of a 400-meter dash, and a diverse set of jumping skills, such as standing long jumps and high jumps.

2025-01-01

Int. J. Robotics Res. (publié)

doi.org

arxiv.org

Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference

Matthew Riemer

Gopeshh Raaj Subbaraj

Glen Berseth

Irina Rish

Realtime environments change even as agents perform action inference and learning, thus requiring high interaction frequencies to effectivel… (voir plus)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.

2024-12-18

ArXiv (prépublication)

arxiv.org

Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference

Matthew Riemer

Gopeshh Raaj Subbaraj

Glen Berseth

Irina Rish

Realtime environments change even as agents perform action inference and learning, thus requiring high interaction frequencies to effectivel… (voir plus)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.

2024-12-18

ArXiv (prépublication)

doi.org

arxiv.org

Avantage IA

Développement du groupe d'experts de l'ONU sur l'IA

Bourse de recherche en politiques de l'IA de Mila

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Développement du groupe d'experts de l'ONU sur l'IA

Glen Berseth

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Avantage IA

Développement du groupe d'experts de l'ONU sur l'IA

Bourse de recherche en politiques de l'IA de Mila

Avantage IA

Développement du groupe d'experts de l'ONU sur l'IA

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Glen Berseth

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