Pablo Samuel Castro

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

Roger Creus Castanyer

In recent years, with the increase in the compute power of GPUs, parallelized data collection has become the dominant approach for training … (voir plus)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.

2025-06-22

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

openreview.net

Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning

Roger Creus Castanyer

Johan Samir Obando Ceron

Li Li

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.

2025-06-18

ArXiv (prépublication)

doi.org

arxiv.org

Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning

Roger Creus Castanyer

Johan Samir Obando Ceron

Li Li

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.

2025-06-18

ArXiv (prépublication)

doi.org

arxiv.org

The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning

Jiashun Liu

Johan Samir Obando Ceron

Pablo Samuel Castro

Aaron Courville

Ling Pan

Off-policy deep reinforcement learning (RL) typically leverages replay buffers for reusing past experiences during learning. This can help i… (voir plus)mprove sample efficiency when the collected data is informative and aligned with the learning objectives; when that is not the case, it can have the effect of"polluting"the replay buffer with data which can exacerbate optimization challenges in addition to wasting environment interactions due to wasteful sampling. We argue that sampling these uninformative and wasteful transitions can be avoided by addressing the sunk cost fallacy, which, in the context of deep RL, is the tendency towards continuing an episode until termination. To address this, we propose learn to stop (LEAST), a lightweight mechanism that enables strategic early episode termination based on Q-value and gradient statistics, which helps agents recognize when to terminate unproductive episodes early. We demonstrate that our method improves learning efficiency on a variety of RL algorithms, evaluated on both the MuJoCo and DeepMind Control Suite benchmarks.

2025-06-16

ArXiv (prépublication)

doi.org

arxiv.org

The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning

Jiashun Liu

Johan Samir Obando Ceron

Pablo Samuel Castro

Aaron Courville

Ling Pan

Off-policy deep reinforcement learning (RL) typically leverages replay buffers for reusing past experiences during learning. This can help i… (voir plus)mprove sample efficiency when the collected data is informative and aligned with the learning objectives; when that is not the case, it can have the effect of"polluting"the replay buffer with data which can exacerbate optimization challenges in addition to wasting environment interactions due to wasteful sampling. We argue that sampling these uninformative and wasteful transitions can be avoided by addressing the sunk cost fallacy, which, in the context of deep RL, is the tendency towards continuing an episode until termination. To address this, we propose learn to stop (LEAST), a lightweight mechanism that enables strategic early episode termination based on Q-value and gradient statistics, which helps agents recognize when to terminate unproductive episodes early. We demonstrate that our method improves learning efficiency on a variety of RL algorithms, evaluated on both the MuJoCo and DeepMind Control Suite benchmarks.

2025-06-16

ArXiv (prépublication)

doi.org

arxiv.org

Meta-World+: An Improved, Standardized, RL Benchmark

Reginald McLean

Evangelos Chatzaroulas

Luc McCutcheon

Frank Röder

Tianhe Yu

Zhanpeng He

K.R. Zentner

Ryan Julian

J K Terry

Isaac Woungang

Nariman Farsad

Pablo Samuel Castro

Multi-task reinforcement learning challenges agents to master diverse skills simultaneously, and Meta-World emerged as the gold standard ben… (voir plus)chmark for evaluating these algorithms. However, since the introduction of the Meta-World benchmark there have been numerous undocumented changes which inhibit fair comparison of multi-task and meta reinforcement learning algorithms. This work strives to disambiguate these results from the literature, while also producing an open-source version of Meta-World that has full reproducibility of past results.

2025-06-09

ICML.cc/2025/Workshop/CODEML (spotlight)

openreview.net

Continual Learning in Vision-Language Models via Aligned Model Merging

Ghada Sokar

Gintare Karolina Dziugaite

Anurag Arnab

Ahmet Iscen

Pablo Samuel Castro

Cordelia Schmid

Continual learning is conventionally tackled through sequential fine-tuning, a process that, while enabling adaptation, inherently favors pl… (voir plus)asticity over the stability needed to retain prior knowledge. While existing approaches attempt to mitigate catastrophic forgetting, a bias towards recent tasks persists as they build upon this sequential nature. In this work we present a new perspective based on model merging to maintain stability while still retaining plasticity. Rather than just sequentially updating the model weights, we propose merging newly trained task parameters with previously learned ones, promoting a better balance. To maximize the effectiveness of the merging process, we propose a simple mechanism that promotes learning aligned weights with previous ones, thereby avoiding interference when merging. We evaluate this approach on large Vision-Language Models (VLMs), and demonstrate its effectiveness in reducing forgetting, increasing robustness to various task orders and similarities, and improving generalization.

2025-06-01

arXiv (publié)

doi.org

arxiv.org

Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning

Roger Creus Castanyer

Johan Samir Obando Ceron

Li Li

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.

2025-06-01

arXiv (publié)

doi.org

arxiv.org

Continual Learning in Vision-Language Models via Aligned Model Merging

Ghada Sokar

Gintare Karolina Dziugaite

Anurag Arnab

Ahmet Iscen

Pablo Samuel Castro

Cordelia Schmid

Continual learning is conventionally tackled through sequential fine-tuning, a process that, while enabling adaptation, inherently favors pl… (voir plus)asticity over the stability needed to retain prior knowledge. While existing approaches attempt to mitigate catastrophic forgetting, a bias towards recent tasks persists as they build upon this sequential nature. In this work we present a new perspective based on model merging to maintain stability while still retaining plasticity. Rather than just sequentially updating the model weights, we propose merging newly trained task parameters with previously learned ones, promoting a better balance. To maximize the effectiveness of the merging process, we propose a simple mechanism that promotes learning aligned weights with previous ones, thereby avoiding interference when merging. We evaluate this approach on large Vision-Language Models (VLMs), and demonstrate its effectiveness in reducing forgetting, increasing robustness to various task orders and similarities, and improving generalization.

2025-05-30

ArXiv (prépublication)

arxiv.org

Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy

Max Schwarzer

Jesse Farebrother

Joshua Greaves

Ekin Dogus Cubuk

Rishabh Agarwal

Aaron Courville

Marc Gendron-Bellemare

Sergei Kalinin

Igor Mordatch

Pablo Samuel Castro

Kevin M Roccapriore

We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimu… (voir plus)lated by the electron beam of a scanning transmission electron microscope (STEM). Our method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition probabilities. These learned transition dynamics are then leveraged to guide a single silicon atom throughout the lattice to pre-determined target destinations. We present empirical analyses that demonstrate the efficacy and generality of our approach.

2025-05-20

Advanced Materials Interfaces (publié)

doi.org

arxiv.org

Multi-Task Reinforcement Learning Enables Parameter Scaling

Reginald McLean

Evangelos Chatzaroulas

J K Terry

Isaac Woungang

Nariman Farsad

Pablo Samuel Castro

Multi-task reinforcement learning (MTRL) aims to endow a single agent with the ability to perform well on multiple tasks. Recent works have … (voir plus)focused on developing novel sophisticated architectures to improve performance, often resulting in larger models; it is unclear, however, whether the performance gains are a consequence of the architecture design or the extra parameters. We argue that gains are mostly due to scale by demonstrating that naively scaling up a simple MTRL baseline to match parameter counts outperforms the more sophisticated architectures, and these gains benefit most from scaling the critic over the actor. Additionally, we explore the training stability advantages that come with task diversity, demonstrating that increasing the number of tasks can help mitigate plasticity loss. Our findings suggest that MTRL's simultaneous training across multiple tasks provides a natural framework for beneficial parameter scaling in reinforcement learning, challenging the need for complex architectural innovations.

2025-05-09

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

openreview.net

Multi-Task Reinforcement Learning Enables Parameter Scaling

Reginald McLean

Evangelos Chatzaroulas

J K Terry

Isaac Woungang

Nariman Farsad

Pablo Samuel Castro

Multi-task reinforcement learning (MTRL) aims to endow a single agent with the ability to perform well on multiple tasks. Recent works have … (voir plus)focused on developing novel sophisticated architectures to improve performance, often resulting in larger models; it is unclear, however, whether the performance gains are a consequence of the architecture design or the extra parameters. We argue that gains are mostly due to scale by demonstrating that naively scaling up a simple MTRL baseline to match parameter counts outperforms the more sophisticated architectures, and these gains benefit most from scaling the critic over the actor. Additionally, we explore the training stability advantages that come with task diversity, demonstrating that increasing the number of tasks can help mitigate plasticity loss. Our findings suggest that MTRL's simultaneous training across multiple tasks provides a natural framework for beneficial parameter scaling in reinforcement learning, challenging the need for complex architectural innovations.

2025-05-09

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

openreview.net

Conférence d'ouverture | Créer une IA plus sécuritaire pour la santé mentale des jeunes

TRAIL : IA responsable pour les professionnels et les leaders

Fondateur en résidence Mila Ventures

Éclaireurs autochtones en IA

Pablo Samuel Castro

Biographie

Étudiants actuels

Billets de blogue

Publications

Conférence d'ouverture | Créer une IA plus sécuritaire pour la santé mentale des jeunes

TRAIL : IA responsable pour les professionnels et les leaders

Fondateur en résidence Mila Ventures

Éclaireurs autochtones en IA

Mots-clés populaires:

Pablo Samuel Castro

Biographie

Étudiants actuels

Billets de blogue

Publications