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arXiv · 1809.06098

Policy Optimization via Importance Sampling

Alberto Maria Metelli·Matteo Papini·Francesco Faccio·Marcello Restelli
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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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Alberto Maria Metelli, Matteo Papini, Francesco Faccio, Marcello Restelli. 2018-09-17. Policy Optimization via Importance Sampling. https://arxiv.org/abs/1809.06098

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