Portrait de Thomas Jiralerspong

Thomas Jiralerspong

Doctorat - UdeM
Superviseur⋅e principal⋅e
Guillaume Lajoie
Co-supervisor
Yoshua Bengio
Sujets de recherche
Apprentissage de représentations
Apprentissage par renforcement
Apprentissage profond
Modèles génératifs
Raisonnement
Théorie de l'information
Site web
Google Scholar
GitHub

Publications

Efficient Causal Graph Discovery Using Large Language Models
Thomas Jiralerspong
Xiaoyin Chen
Yash More
Vedant Shah
Yoshua Bengio
2024-03-05
ICLR.cc/2024/Workshop/AGI (poster)
doi.org
openreview.net
Efficient Causal Graph Discovery Using Large Language Models
Thomas Jiralerspong
Xiaoyin Chen
Yash More
Vedant Shah
Yoshua Bengio
We propose a novel framework that leverages LLMs for full causal graph discovery. While previous LLM-based methods have used a pairwise quer… (voir plus)y approach, this requires a quadratic number of queries which quickly becomes impractical for larger causal graphs. In contrast, the proposed framework uses a breadth-first search (BFS) approach which allows it to use only a linear number of queries. We also show that the proposed method can easily incorporate observational data when available, to improve performance. In addition to being more time and data-efficient, the proposed framework achieves state-of-the-art results on real-world causal graphs of varying sizes. The results demonstrate the effectiveness and efficiency of the proposed method in discovering causal relationships, showcasing its potential for broad applicability in causal graph discovery tasks across different domains.
2024-02-02
ArXiv (prépublication)
doi.org
arxiv.org
Forecaster: Towards Temporally Abstract Tree-Search Planning from Pixels
Thomas Jiralerspong
Flemming Kondrup
Doina Precup
Khimya Khetarpal
The ability to plan at many different levels of abstraction enables agents to envision the long-term repercussions of their decisions and th… (voir plus)us enables sample-efficient learning. This becomes particularly beneficial in complex environments from high-dimensional state space such as pixels, where the goal is distant and the reward sparse. We introduce Forecaster, a deep hierarchical reinforcement learning approach which plans over high-level goals leveraging a temporally abstract world model. Forecaster learns an abstract model of its environment by modelling the transitions dynamics at an abstract level and training a world model on such transition. It then uses this world model to choose optimal high-level goals through a tree-search planning procedure. It additionally trains a low-level policy that learns to reach those goals. Our method not only captures building world models with longer horizons, but also, planning with such models in downstream tasks. We empirically demonstrate Forecaster's potential in both single-task learning and generalization to new tasks in the AntMaze domain.
2023-10-27
NeurIPS.cc/2023/Workshop/GenPlan (publié)
doi.org
openreview.net
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL
Chen Sun
Wannan Yang
Thomas Jiralerspong
Dane Malenfant
Benjamin Alsbury-Nealy
Yoshua Bengio
Blake Richards
In real life, success is often contingent upon multiple critical steps that are distant in time from each other and from the final reward. T… (voir plus)hese critical steps are challenging to identify with traditional reinforcement learning (RL) methods that rely on the Bellman equation for credit assignment. Here, we present a new RL algorithm that uses offline contrastive learning to hone in on these critical steps. This algorithm, which we call Contrastive Retrospection (ConSpec), can be added to any existing RL algorithm. ConSpec learns a set of prototypes for the critical steps in a task by a novel contrastive loss and delivers an intrinsic reward when the current state matches one of the prototypes. The prototypes in ConSpec provide two key benefits for credit assignment: (i) They enable rapid identification of all the critical steps. (ii) They do so in a readily interpretable manner, enabling out-of-distribution generalization when sensory features are altered. Distinct from other contemporary RL approaches to credit assignment, ConSpec takes advantage of the fact that it is easier to retrospectively identify the small set of steps that success is contingent upon (and ignoring other states) than it is to prospectively predict reward at every taken step. ConSpec greatly improves learning in a diverse set of RL tasks. The code is available at the link: https://github.com/sunchipsster1/ConSpec
openreview.net
Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning
Flemming Kondrup
Thomas Jiralerspong
Elaine Lau
Nathan de Lara
Jacob A. Shkrob
My Duc Tran
Doina Precup
Sumana Basu
2022-10-05
ArXiv (prépublication)
doi.org
arxiv.org
Contrastive introspection (ConSpec) to rapidly identify invariant prototypes for success in RL
Chen Sun
Mila
Wannan Yang
Benjamin Alsbury-Nealy
Thomas Jiralerspong
Yoshua Bengio
†. BlakeRichards
Reinforcement learning (RL) algorithms have achieved notable success in recent years, but still struggle with fundamental issues in long-ter… (voir plus)m credit assignment. It remains difficult to learn in situations where success is contingent upon multiple critical steps that are distant in time from each other and from a sparse reward; as is often the case in real life. Moreover, how RL algorithms assign credit in these difficult situations is typically not coded in a way that can rapidly generalize to new situations. Here, we present an approach using offline contrastive learning, which we call contrastive introspection (ConSpec), that can be added to any existing RL algorithm and addresses both issues. In ConSpec, a contrastive loss is used during offline replay to identify invariances among successful episodes. This takes advantage of the fact that it is easier to retrospectively identify the small set of steps that success is contingent upon than it is to prospectively predict reward at every step taken in the environment. ConSpec stores this knowledge in a collection of prototypes summarizing the intermediate states required for success. During training, arrival at any state that matches these prototypes generates an intrinsic reward that is added to any external rewards. As well, the reward shaping provided by ConSpec can be made to preserve the optimal policy of the underlying RL agent. The prototypes in ConSpec provide two key benefits for credit assignment: (1) They enable rapid identification of all the critical states. (2) They do so in a readily interpretable manner, enabling out of distribution generalization when sensory features are altered. In summary, ConSpec is a modular system that can be added to any existing RL algorithm to improve its long-term credit assignment.