Non-convex optimization with local search heuristics has been widely used in machine learning, achieving many state-of-art results. It becomes increasingly important to understand why they can work for these NP-hard problems on typical data. The landscape of many objective functions in learning has been conjectured to have the geometric property that ``all local optima are (approximately) global optima'', and thus they can be solved efficiently by local search algorithms. However, establishing such property can be very difficult. In this paper, we analyze the optimization landscape of the random over-complete tensor decomposition problem, which has many applications in unsupervised leaning, especially in learning latent variable models.

Whenrewardsare fully observed, we show that the expected reward objectiveexhibits suboptimal plateaus and exponentially many local optima in the worst case.

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DDPG [24]extends Q-learning to continuous control based on the Deterministic Policy Gradient [31] algorithm, which learns a deterministic policyπ(s;φ) parameterized byφto maximize the Q-function to approximate themaxoperator.