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.
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Interdisciplinary Bridge
- Machine Learning and Reinforcement Learning
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Hot Topic Early Bird
- policy optimization
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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