Policy Optimization via Importance Sampling

Abstract
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.

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 @incollection{metelli2018policy,
    author = "Metelli, Alberto Maria and Papini, Matteo and Faccio, Francesco and Restelli, Marcello",
    title = "Policy Optimization via Importance Sampling",
    booktitle = "Advances in Neural Information Processing Systems ({NeurIPS})",
    pages = "5447--5459",
    year = "2018",
    url = "http://papers.nips.cc/paper/7789-policy-optimization-via-importance-sampling"
}