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2020 ICML ICML 2020

Taylor Expansion Policy Optimization

Abstract

In this work, we investigate the application of Taylor expansions in reinforcement learning. In particular, we propose Taylor Expansion Policy Optimization, a policy optimization formalism that generalizes prior work as a first-order special case. We also show that Taylor expansions intimately relate to off-policy evaluation. Finally, we show that this new formulation entails modifications which improve the performance of several state-of-the-art distributed algorithms.

🌉 Interdisciplinary Bridge — Machine Learning and Reinforcement Learning
🐣 Hot Topic Early Bird — policy optimization
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio

Authors

Yunhao Tang , Michal Valko , Rémi Munos

Topics

Machine Learning > Optimization & Theory > Neural Network Optimization Machine Learning > Optimization & Theory > Optimization Reinforcement Learning > Methods > Policy Learning Deep Learning > Optimization & Theory > Optimization

Keywords

reinforcement learning off-policy evaluation policy optimization neural network optimization distributed algorithm taylor expansion
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