Efficient Reinforcement Learning Development with RLzoo
This addresses the problem of low efficiency in DRL development for researchers and developers, though it is incremental as it builds on existing DRL libraries.
The paper tackles the challenge of inefficient development of deep reinforcement learning (DRL) agents by introducing RLzoo, a new library that provides high-level APIs, a model zoo, and an automatic construction algorithm, resulting in reduced development cost while maintaining comparable performance to existing libraries.
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.