Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Wasserstein Robust Reinforcement Learning   Reinforcement learning algorithms, though successful, tend to over-fit to
training environments hampering their application to the real-world. This paper
proposes $\text{W}\text{R}^{2}\text{L}$ -- a robust reinforcement learning
algorithm with significant robust performance on low and high-dimensional
control tasks. Our method formalises robust reinforcement learning as a novel
min-max game with a Wasserstein constraint for a correct and convergent solver.
Apart from the formulation, we also propose an efficient and scalable solver
following a novel zero-order optimisation method that we believe can be useful
to numerical optimisation in general. We empirically demonstrate significant
gains compared to standard and robust state-of-the-art algorithms on
high-dimensional MuJuCo environments.

17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org