  Policy optimization is an effective reinforcement learning approach to solve
continuous control tasks. Recent achievements have shown that alternating
online and offline optimization is a successful choice for efficient trajectory
reuse. However, deciding when to stop optimizing and collect new trajectories
is non-trivial, as it requires to account for the variance of the objective
function estimate. In this paper, we propose a novel, model-free, policy search
algorithm, POIS, applicable in both action-based and parameter-based settings.
We first derive a high-confidence bound for importance sampling estimation;
then we define a surrogate objective function, which is optimized offline
whenever a new batch of trajectories is collected. Finally, the algorithm is
tested on a selection of continuous control tasks, with both linear and deep
policies, and compared with state-of-the-art policy optimization methods.
