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Mastering URLB Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels Sai Rajeswar* Pietro Mazzaglia* Tim Verbelen Alexandre Piché Bart Dhoedt Aaron Courville Alexandre Lacoste * Equal contribution Article Code Abstract Controlling artificial agents from visual sensory data is an arduous task. Reinforcement learning (RL) algorithms can succeed but require large amounts of interactions between the agent and the environment. To alleviate the issue, unsupervised RL proposes to employ self-supervised interaction and learning, for adapting faster to future tasks. Yet, as shown in the Unsupervised RL Benchmark (URLB; Laskin et al. 2021), whether current unsupervised strategies can improve generalization capabilities is still unclear, especially in visual control settings. In this work, we study the URLB and propose a new method to solve it, using unsupervis...
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