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Unsupervised Learning and Exploration of Reachable Outcome Space

Abstract : Performing Reinforcement Learning in sparse rewards settings, with very little prior knowledge, is a challenging problem since there is no signal to properly guide the learning process. In such situations, a good search strategy is fundamental. At the same time, not having to adapt the algorithm to every single problem is very desirable. Here we introduce TAXONS, a Task Agnostic eXploration of Outcome spaces through Novelty and Surprise algorithm. Based on a population-based divergent-search approach, it learns a set of diverse policies directly from high-dimensional observations, without any task-specific information. TAXONS builds a repertoire of policies while training an autoencoder on the high-dimensional observation of the final state of the system to build a low-dimensional outcome space. The learned outcome space, combined with the reconstruction error, is used to drive the search for new policies. Results show that TAXONS can find a diverse set of controllers, covering a good part of the ground-truth outcome space, while having no information about such space.
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https://hal.archives-ouvertes.fr/hal-02951255
Contributor : Giuseppe Paolo <>
Submitted on : Monday, September 28, 2020 - 3:52:10 PM
Last modification on : Tuesday, March 30, 2021 - 5:47:56 PM

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Giuseppe Paolo, Alban Laflaquière, Alexandre Coninx, Stephane Doncieux. Unsupervised Learning and Exploration of Reachable Outcome Space. IEEE International Conference on Robotics and Automation (ICRA), 2020, Paris, France. ⟨10.1109/icra40945.2020.9196819⟩. ⟨hal-02951255⟩

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