Commit 53030137 authored by Daniel Lukats's avatar Daniel Lukats

added updated abstract

parent e2e784b0
......@@ -2,7 +2,7 @@
\section*{Abstract}
Reinforcement learning denotes a class of machine learning algorithms that train an agent through trial-and-error.
Instead of correcting the agent's behavior, it is merely rewarded when it achieves a set out goal and punished when it
Instead of correcting the agent's behavior, it merely is rewarded when it achieves a set out goal and punished when it
fails to do so.
Recently, reinforcement learning was combined with deep learning to utilize neural networks. \emph{Proximal Policy
......@@ -17,5 +17,5 @@ task for reinforcement learning algorithms.
The experiments reveal that most optimization choices have a strong effect on the performance of Proximal Policy
Optimization. Furthermore, they support the authors' claims that the algorithm is robust to configuration choices.
Finally, notable outliers are apparent in approximately 35\% of the experiments. As a consequence, reproducibility of
the results can be a challenge.
Notable outliers are apparent in approximately 35\% of the experiments. As a consequence, reproducing the original
results can pose a challenge.
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