Multi-task Reinforcement Learning with a Planning Quasi-Metric

NeurIPS 2020

Multi-task Reinforcement Learning with a Planning Quasi-Metric

Dec 06, 2020
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We introduce a new reinforcement learning approach combining a planning quasi-metric (PQM) that estimates the number of actions required to go from a state to another, with task-specific planners that compute a target state to reach a given goal. The main advantage of this decomposition is to allow the sharing across tasks of a task-agnostic model of the quasi-metric that captures the environment's dynamics and can be learned in a dense and unsupervised manner. We demonstrate the usefulness of this approach on the standard bit-flip problem and in the MuJoCo robotic arm simulator. Speakers: Vincent Micheli, Karthigan Sinnathamby, Francois Fleuret

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