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Maxime Wabartha

wabarthm@mila.quebec
Doctorat - McGill University
Superviseur⋅e principal⋅e
Joelle Pineau

Publications

Piecewise Linear Parametrization of Policies: Towards Interpretable Deep Reinforcement Learning
Maxime Wabartha
Joelle Pineau
Learning inherently interpretable policies is a central challenge in the path to developing autonomous agents that humans can trust. Linear … (voir plus)policies can justify their decisions while interacting in a dynamic environment, but their reduced expressivity prevents them from solving hard tasks. Instead, we argue for the use of piecewise-linear policies. We carefully study to what extent they can retain the interpretable properties of linear policies while reaching competitive performance with neural baselines. In particular, we propose the HyperCombinator (HC), a piecewise-linear neural architecture expressing a policy with a controllably small number of sub-policies. Each sub-policy is linear with respect to interpretable features, shedding light on the decision process of the agent without requiring an additional explanation model. We evaluate HC policies in control and navigation experiments, visualize the improved interpretability of the agent and highlight its trade-off with performance. Moreover, we validate that the restricted model class that the HyperCombinator belongs to is compatible with the algorithmic constraints of various reinforcement learning algorithms.
2024-01-16
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
Piecewise Linear Parametrization of Policies: Towards Interpretable Deep Reinforcement Learning
Maxime Wabartha
Joelle Pineau
2024-01-16
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
Handling Black Swan Events in Deep Learning with Diversely Extrapolated Neural Networks
Maxime Wabartha
Audrey Durand
Vincent Francois-Lavet
Joelle Pineau
By virtue of their expressive power, neural networks (NNs) are well suited to fitting large, complex datasets, yet they are also known to … (voir plus)produce similar predictions for points outside the training distribution. As such, they are, like humans, under the influence of the Black Swan theory: models tend to be extremely "surprised" by rare events, leading to potentially disastrous consequences, while justifying these same events in hindsight. To avoid this pitfall, we introduce DENN, an ensemble approach building a set of Diversely Extrapolated Neural Networks that fits the training data and is able to generalize more diversely when extrapolating to novel data points. This leads DENN to output highly uncertain predictions for unexpected inputs. We achieve this by adding a diversity term in the loss function used to train the model, computed at specific inputs. We first illustrate the usefulness of the method on a low-dimensional regression problem. Then, we show how the loss can be adapted to tackle anomaly detection during classification, as well as safe imitation learning problems.
2020-07-01
International Joint Conference on Artificial Intelligence (publié)
doi.org