2 research outputs found
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning
We propose to learn to distinguish reversible from irreversible actions for
better informed decision-making in Reinforcement Learning (RL). From
theoretical considerations, we show that approximate reversibility can be
learned through a simple surrogate task: ranking randomly sampled trajectory
events in chronological order. Intuitively, pairs of events that are always
observed in the same order are likely to be separated by an irreversible
sequence of actions. Conveniently, learning the temporal order of events can be
done in a fully self-supervised way, which we use to estimate the reversibility
of actions from experience, without any priors. We propose two different
strategies that incorporate reversibility in RL agents, one strategy for
exploration (RAE) and one strategy for control (RAC). We demonstrate the
potential of reversibility-aware agents in several environments, including the
challenging Sokoban game. In synthetic tasks, we show that we can learn control
policies that never fail and reduce to zero the side-effects of interactions,
even without access to the reward function
Machine Learning for Instance Segmentation
Volumetric Electron Microscopy images can be used for connectomics, the study of brain connectivity at the cellular level.
A prerequisite for this inquiry is the automatic identification of neural cells, which requires machine learning algorithms and in particular efficient image segmentation algorithms.
In this thesis, we develop new algorithms for this task.
In the first part we provide, for the first time in this
field, a method for training a neural network to predict optimal input data for a watershed algorithm.
We demonstrate its superior performance compared to other segmentation methods of its category.
In the second part, we develop an efficient watershed-based algorithm for weighted graph
partitioning, the \emph{Mutex Watershed}, which uses negative edge-weights for the first time.
We show that it is intimately related to the multicut and has a cutting edge performance on a connectomics challenge.
Our algorithm is currently used by the leaders of two connectomics challenges.
Finally, motivated by inpainting neural networks, we create a method to learn the graph weights without any supervision