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Zina Kamel

Alumni

Publications

Gated Transformer World Models for Model-Based Reinforcement Learning
Zina Kamel
Tabitha Edith Lee
Glen Berseth
Hsiu-Chin Lin
David Meger
Model-based reinforcement learning has emerged as a promising approach for training sample-efficient policies by learning compact latent dyn… (voir plus)amics models that simulate, plan, and act through imagination. Recent work has shown that transformer-based world models outperform recurrent architectures due to their ability to capture long-range temporal dependencies. However, standard self-attention mechanisms apply the attention score uniformly across all dimensions of the token's representation, lacking an explicit mechanism to regulate information flow across dimensions. We introduce a gating mechanism for transformer-based world models that implements a feature-wise modulation mechanism within the attention computation. The gating module adaptively scales dimensions in a data-dependent way, enabling the model to selectively control contextual information. Our gating mechanism achieves greater sample-efficient policy learning through normalized mean performance improvements for the Atari 100k benchmark when applied to the STORM and TWISTER transformer world models.
2026-10-02
WM_PAI @ Advances in Neural Information Processing Systems (poster)
openreview.net
openreview.net
DIVERS-Bench: Evaluating Language Identification Across Domain Shifts and Code-Switching
Jessica Ojo
Zina Kamel
David Ifeoluwa Adelani
2025-09-21
arXiv (prépublication)
doi.org
arxiv.org
Scalable Tree Search over Graphs with Learned Action Pruning for Power Grid Control
Florence Cloutier
Cyrus Neary
Adriana Hugessen
Viktor Todosijevic
Zina Kamel
Glen Berseth
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.
2025-06-16
rl-conference.cc/RLC/2025/Workshop/RL4RS (publié)
openreview.net
openreview.net