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Abstract

Policy search is a successful approach to reinforcement learning. However, policy
improvements often result in the loss of information. Hence, it has been marred by
premature convergence and implausible solutions. As first suggested in the context of
covariant policy gradients, many of these problems may be addressed by constraining
the information loss. In this book chapter, we continue this path of reasoning and suggest
the Relative Entropy Policy Search (REPS) method. The resulting method differs
significantly from previous policy gradient approaches and yields an exact update step.
It works well on typical reinforcement learning benchmark problems. We will also
present a real-world applications where a robot employs REPS to learn how to return balls in a game of table tennis.