Reinforcement learning in robotics: A survey <s> Non-parametric policies <s> (Please note: The main body of this paper is written in Japanese) When humans interact in a social context, there are many factors apart from the actual communication that need to be considered. Pr ... <s> BIB001 </s> Reinforcement learning in robotics: A survey <s> Non-parametric policies <s> 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 (Bagnell and Schneider 2003), many of these problems may be addressed by constraining the information loss. In this paper, 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. <s> BIB002
Polices based on nonparametric regression approaches often allow a more datadriven learning process. This approach is often preferable over the purely parametric policies listed above because the policy structure can evolve during the learning process. Such approaches are especially useful when a policy learned to adjust the existing behaviors of a lower-level controller, such as when choosing among different robothuman interaction possibilities BIB001 , selecting among different striking movements in a table tennis task BIB002 , and setting the meta-actions for dart throwing and table tennis hitting tasks .