  Deep reinforcement learning enables algorithms to learn complex behavior,
deal with continuous action spaces and find good strategies in environments
with high dimensional state spaces. With deep reinforcement learning being an
active area of research and many concurrent inventions, we decided to focus on
a relatively simple robotic task to evaluate a set of ideas that might help to
solve recent reinforcement learning problems. We test a newly created
combination of two commonly used reinforcement learning methods, whether it is
able to learn more effectively than a baseline. We also compare different ideas
to preprocess information before it is fed to the reinforcement learning
algorithm. The goal of this strategy is to reduce training time and eventually
help the algorithm to converge. The concluding evaluation proves the general
applicability of the described concepts by testing them using a simulated
environment. These concepts might be reused for future experiments.
