You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Reinforcement learning in robotics: A survey 
Chapter Title:  Policy search with learned models 
1. Abstract of Cited Paper (BIB001):  Gait optimization is a basic yet challenging problem for both quadrupedal and bipedal robots. Although techniques for automating the process exist, most involve local function optimization procedures that suffer from three key drawbacks. Local optimization techniques are naturally plagued by local optima, make no use of the expensive gait evaluations once a local step is taken, and do not explicitly model noise in gait evaluation. These drawbacks increase the need for a large number of gait evaluations, making optimization slow, data inefficient, and manually intensive. We present a Bayesian approach based on Gaussian process regression that addresses all three drawbacks. It uses a global search strategy based on a posterior model inferred from all of the individual noisy evaluations. We demonstrate the technique on a quadruped robot, using it to optimize two different criteria: speed and smoothness. We show in both cases our technique requires dramatically fewer gait evaluations than state-of-the-art local gradient approaches. 
2. Abstract of Cited Paper (BIB002):  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.

Literature Review:
Similarly as for value-function methods, all model-free policy-search methods can be used in conjunction with learned simulators. For example, both pairwise comparisons of policies and policy gradient approaches have been used with learned simulators BIB001  Ng et al., 2004a,b) . Transferring EM-like policy search and relative entropy policy search BIB003 appears to be a natural next step. Nevertheless, as mentioned in Section 6.1, a series of policy update methods has been suggested that were tailored for probabilistic simulation BIB002 . There still appears to be the need for new methods that make better use of the model knowledge, both in policysearch and for value-function methods.