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Iterative Amortized Policy Optimization
Joseph Marino · Alexandre Piche · Alessandro Davide Ialongo · Yisong Yue

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @

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 amortized optimization, optimizing network parameters rather than the policy distributions directly. However, 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 iterative amortized optimizers. We demonstrate that the resulting technique, iterative amortized policy optimization, yields performance improvements over direct amortization on benchmark continuous control tasks.

Author Information

Joseph Marino (DeepMind)
Alexandre Piche (Mila)
Alessandro Davide Ialongo (University of Cambridge)
Yisong Yue (Caltech)

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