Iterative Amortized Policy Optimization

• URL: http://arxiv.org/abs/2010.10670v2
• Date: Fri, 22 Oct 2021 20:44:57 GMT
• Title: Iterative Amortized Policy Optimization
• Authors: Joseph Marino, Alexandre Pich\'e, Alessandro Davide Ialongo, Yisong Yue
• Abstract summary: Policy networks are a central feature of deep reinforcement learning (RL) algorithms for continuous control. From the variational inference perspective, policy networks are a form of textitamortized optimization, optimizing network parameters rather than the policy distributions directly. We demonstrate that iterative amortized policy optimization, yields performance improvements over direct amortization on benchmark continuous control tasks.
• Score: 147.63129234446197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Policy networks are a central feature of deep reinforcement learning (RL) algorithms for continuous control, enabling the estimation and sampling of high-value actions. From the variational inference perspective on RL, policy networks, when used with entropy or KL regularization, are a form of \textit{amortized optimization}, optimizing network parameters rather than the policy distributions directly. However, \textit{direct} amortized mappings can yield suboptimal policy estimates and restricted distributions, limiting performance and exploration. Given this perspective, we consider the more flexible class of \textit{iterative} amortized optimizers. We demonstrate that the resulting technique, iterative amortized policy optimization, yields performance improvements over direct amortization on benchmark continuous control tasks.

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