  Policy optimization methods are powerful algorithms in Reinforcement Learning
(RL) for their flexibility to deal with policy parameterization and ability to
handle model misspecification. However, these methods usually suffer from slow
convergence rates and poor sample complexity. Hence it is important to design
provably sample efficient algorithms for policy optimization. Yet, recent
advances for this problems have only been successful in tabular and linear
setting, whose benign structures cannot be generalized to non-linearly
parameterized policies. In this paper, we address this problem by leveraging
recent advances in value-based algorithms, including bounded eluder-dimension
and online sensitivity sampling, to design a low-switching sample-efficient
policy optimization algorithm, LPO, with general non-linear function
approximation. We show that, our algorithm obtains an $\varepsilon$-optimal
policy with only $\widetilde{O}(\frac{\text{poly}(d)}{\varepsilon^3})$ samples,
where $\varepsilon$ is the suboptimality gap and $d$ is a complexity measure of
the function class approximating the policy. This drastically improves
previously best-known sample bound for policy optimization algorithms,
$\widetilde{O}(\frac{\text{poly}(d)}{\varepsilon^8})$. Moreover, we empirically
test our theory with deep neural nets to show the benefits of the theoretical
inspiration.
