Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hyperparameter Auto-tuning in Self-Supervised Robotic Learning   Policy optimization in reinforcement learning requires the selection of
numerous hyperparameters across different environments. Fixing them incorrectly
may negatively impact optimization performance leading notably to insufficient
or redundant learning. Insufficient learning (due to convergence to local
optima) results in under-performing policies whilst redundant learning wastes
time and resources. The effects are further exacerbated when using single
policies to solve multi-task learning problems. Observing that the Evidence
Lower Bound (ELBO) used in Variational Auto-Encoders correlates with the
diversity of image samples, we propose an auto-tuning technique based on the
ELBO for self-supervised reinforcement learning. Our approach can auto-tune
three hyperparameters: the replay buffer size, the number of policy gradient
updates during each epoch, and the number of exploration steps during each
epoch. We use a state-of-the-art self-supervised robot learning framework
(Reinforcement Learning with Imagined Goals (RIG) using Soft Actor-Critic) as
baseline for experimental verification. Experiments show that our method can
auto-tune online and yields the best performance at a fraction of the time and
computational resources. Code, video, and appendix for simulated and real-robot
experiments can be found at the project page \url{www.JuanRojas.net/autotune}.

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The categories of this paper are: nlin
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The categories of this paper are: ao-sci
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: plasm-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: stat
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The categories of this paper are: adap-org
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The categories of this paper are: supr-con
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-th