Investigating the Impact of Action Representations in Policy Gradient
  Algorithms
Jan Schneider Pierre Schumacher Daniel Häufle Bernhard Schölkopf Dieter Büchler
Reinforcement learning~(RL) is a versatile framework for learning to solve
complex real-world tasks. However, influences on the learning performance of RL
algorithms are often poorly understood in practice. We discuss different
analysis techniques and assess their effectiveness for investigating the impact
of action representations in RL. Our experiments demonstrate that the action
representation can significantly influence the learning performance on popular
RL benchmark tasks. The analysis results indicate that some of the performance
differences can be attributed to changes in the complexity of the optimization
landscape. Finally, we discuss open challenges of analysis techniques for RL
algorithms.