Meta-Reinforcement Learning in Non-Stationary and Dynamic Environments

Author:

Bing Zhenshan1ORCID,Lerch David1ORCID,Huang Kai2ORCID,Knoll Alois1ORCID

Affiliation:

1. Department of Informatics, Technical University of Munich, Munich, Germany

2. Sun Yat-sen University, Shenzhen, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Reference28 articles.

1. A simple neural attentive meta-learner;mishra,2017

2. Deep online learning via meta-learning: Continual adaptation for model-based RL;nagabandi,2018

3. MuJoCo: A physics engine for model-based control

4. Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning;yu,2019

5. Learning to adapt in dynamic, real-world environments through meta-reinforcement learning;nagabandi,2018

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