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Clement Gehring

Alumni

Publications

Course Correcting Koopman Representations
Mahan Fathi
Clement Gehring
Jonathan Pilault
David Kanaa
Pierre-Luc Bacon
Ross Goroshin
Koopman representations aim to learn features of nonlinear dynamical systems (NLDS) which lead to linear dynamics in the latent space. Theor… (voir plus)etically, such features can be used to simplify many problems in modeling and control of NLDS. In this work we study autoencoder formulations of this problem, and different ways they can be used to model dynamics, specifically for future state prediction over long horizons. We discover several limitations of predicting future states in the latent space and propose an inference-time mechanism, which we refer to as Periodic Reencoding, for faithfully capturing long term dynamics. We justify this method both analytically and empirically via experiments in low and high dimensional NLDS.
2024-12-30
OpenReview (publié)
doi.org
openreview.net
Do Transformer World Models Give Better Policy Gradients?
Michel Ma
Tianwei Ni
Clement Gehring
Pierluca D’Oro
Pierre-Luc Bacon
A natural approach for reinforcement learning is to predict future rewards by unrolling a neural network world model, and to backpropagate t… (voir plus)hrough the resulting computational graph to learn a policy. However, this method often becomes impractical for long horizons since typical world models induce hard-to-optimize loss landscapes. Transformers are known to efficiently propagate gradients over long horizons: could they be the solution to this problem? Surprisingly, we show that commonly-used transformer world models produce circuitous gradient paths, which can be detrimental to long-range policy gradients. To tackle this challenge, we propose a class of world models called Actions World Models (AWMs), designed to provide more direct routes for gradient propagation. We integrate such AWMs into a policy gradient framework that underscores the relationship between network architectures and the policy gradient updates they inherently represent. We demonstrate that AWMs can generate optimization landscapes that are easier to navigate even when compared to those from the simulator itself. This property allows transformer AWMs to produce better policies than competitive baselines in realistic long-horizon tasks.
2024-12-30
OpenReview (publié)
doi.org
proceedings.mlr.press
Neural differential equations for temperature control in buildings under demand response programs
Vincent Taboga
Clement Gehring
Mathieu Le Cam
Hanane Dagdougui
Pierre-Luc Bacon
2024-07-31
Applied Energy (publié)
doi.org
Bridging State and History Representations: Understanding Self-Predictive RL
Tianwei Ni
Benjamin Eysenbach
Erfan Seyedsalehi
Michel Ma
Clement Gehring
Aditya Mahajan
Pierre-Luc Bacon
Representations are at the core of all deep reinforcement learning (RL) methods for both Markov decision processes (MDPs) and partially obse… (voir plus)rvable Markov decision processes (POMDPs). Many representation learning methods and theoretical frameworks have been developed to understand what constitutes an effective representation. However, the relationships between these methods and the shared properties among them remain unclear. In this paper, we show that many of these seemingly distinct methods and frameworks for state and history abstractions are, in fact, based on a common idea of self-predictive abstraction. Furthermore, we provide theoretical insights into the widely adopted objectives and optimization, such as the stop-gradient technique, in learning self-predictive representations. These findings together yield a minimalist algorithm to learn self-predictive representations for states and histories. We validate our theories by applying our algorithm to standard MDPs, MDPs with distractors, and POMDPs with sparse rewards. These findings culminate in a set of preliminary guidelines for RL practitioners.
2024-05-06
International Conference on Learning Representations (Accept (poster))
doi.org
openreview.net