Deep Online Learning via Meta-Learning : Continual Adaptation for Model-Based RL

By Anusha Nagabandi. 2019

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Gabriel Dulac-Arnold
2. Daniel Jaymin Mankowitz
3. Todd Hester
4. Cosmin Paduraru
5. Peter Stone
6. Sergey Levine
7. Nir Levine
8. Michael Janner

Citations in the corpus

• Online Meta-Learning (2019, neutral): “While some meta-learning works have considered online learning settings at meta-test time (Santoro et al., 2016; Al-Shedivat et al., 2017; Nagabandi et al., 2018), nearly all prior meta-learning algorithms assume that the meta-training tasks come from a stationary distribution.”
• Challenges of Real-World Reinforcement Learning (2019, positive): “System identification involves training a policy that can determine online the environment in which it is operating and modify the policy accordingly (Finn et al., 2017; Nagabandi et al., 2018).”