Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Model-free Reinforcement Learning with Multi-objective Bayesian
  Optimization   In reinforcement learning (RL), an autonomous agent learns to perform complex
tasks by maximizing an exogenous reward signal while interacting with its
environment. In real-world applications, test conditions may differ
substantially from the training scenario and, therefore, focusing on pure
reward maximization during training may lead to poor results at test time. In
these cases, it is important to trade-off between performance and robustness
while learning a policy. While several results exist for robust, model-based
RL, the model-free case has not been widely investigated. In this paper, we
cast the robust, model-free RL problem as a multi-objective optimization
problem. To quantify the robustness of a policy, we use delay margin and gain
margin, two robustness indicators that are common in control theory. We show
how these metrics can be estimated from data in the model-free setting. We use
multi-objective Bayesian optimization (MOBO) to solve efficiently this
expensive-to-evaluate, multi-objective optimization problem. We show the
benefits of our robust formulation both in sim-to-real and pure hardware
experiments to balance a Furuta pendulum.

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The categories of this paper are: hep-ph
2
The categories of this paper are: math
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
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The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-th