Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient 
Estimation for Deep Reinforcement Learning

Gu, S.; Lillicrap, T.; Turner, R. E.; Ghahramani, Z.; Schölkopf, B; Levine, S.

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Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning

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Gu, S.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Schölkopf, B
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Gu, S., Lillicrap, T., Turner, R. E., Ghahramani, Z., Schölkopf, B., & Levine, S. (2018). Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. In I. Guyon, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 30 (pp. 3847-3856). Red Hook, NY: Curran Associates, Inc. Retrieved from https://papers.nips.cc/paper/6974-interpolated-policy-gradient-merging-on-policy-and-off-policy-gradient-estimation-for-deep-reinforcement-learning.

Cite as: https://hdl.handle.net/21.11116/0000-0000-FEA0-D

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