Efficient Reinforcement Learning Development with RLzoo

• URL: http://arxiv.org/abs/2009.08644v2
• Date: Thu, 19 Aug 2021 01:59:29 GMT
• Title: Efficient Reinforcement Learning Development with RLzoo
• Authors: Zihan Ding, Tianyang Yu, Yanhua Huang, Hongming Zhang, Guo Li, Quancheng Guo, Luo Mai and Hao Dong
• Abstract summary: Existing Deep Reinforcement Learning (DRL) libraries provide poor support for prototyping DRL agents. We introduce RLzoo, a new DRL library that aims to make the development of DRL agents efficient.
• Score: 21.31425280231093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many researchers and developers are exploring for adopting Deep Reinforcement Learning (DRL) techniques in their applications. They however often find such an adoption challenging. Existing DRL libraries provide poor support for prototyping DRL agents (i.e., models), customising the agents, and comparing the performance of DRL agents. As a result, the developers often report low efficiency in developing DRL agents. In this paper, we introduce RLzoo, a new DRL library that aims to make the development of DRL agents efficient. RLzoo provides developers with (i) high-level yet flexible APIs for prototyping DRL agents, and further customising the agents for best performance, (ii) a model zoo where users can import a wide range of DRL agents and easily compare their performance, and (iii) an algorithm that can automatically construct DRL agents with custom components (which are critical to improve agent's performance in custom applications). Evaluation results show that RLzoo can effectively reduce the development cost of DRL agents, while achieving comparable performance with existing DRL libraries.

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