Asynchronous framework with Reptile+ algorithm to meta learn partially observable Markov decision process
| Autor: | Viet-Hung Dang, Dang Quang Nguyen, Ngo Anh Vien, TaeChoong Chung |
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| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Computer science business.industry Computation Partially observable Markov decision process 02 engineering and technology Machine learning computer.software_genre Task (computing) Artificial Intelligence Control theory Asynchronous communication 0202 electrical engineering electronic engineering information engineering Reinforcement learning 020201 artificial intelligence & image processing Artificial intelligence Transfer of learning business computer |
| Zdroj: | Applied Intelligence. 50:4050-4062 |
| ISSN: | 1573-7497 0924-669X |
| DOI: | 10.1007/s10489-020-01748-7 |
| Popis: | Meta-learning has recently received much attention in a wide variety of deep reinforcement learning (DRL). In non-meta-learning, we have to train a deep neural network as a controller to learn a specific control task from scratch using a large amount of data. This way of training has shown many limitations in handling different related tasks. Therefore, meta-learning on control domains becomes a powerful tool for transfer learning on related tasks. However, it is widely known that meta-learning requires massive computation and training time. This paper will propose a novel DRL framework, which is called HCGF-R2-DDPG (Hybrid CPU/GPU Framework for Reptile+ and Recurrent Deep Deterministic Policy Gradient). HCGF-R2-DDPG will integrate meta-learning into a general asynchronous training architecture. The proposed framework will allow utilising both CPU and GPU to boost the training speed for the meta network initialisation. We will evaluate HCGF-R2-DDPG on various Partially Observable Markov Decision Process (POMDP) domains. |
| Databáze: | OpenAIRE |
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