Efficient Reinforcement Learning Development with RLzoo

TL;DR

RLzoo is a new DRL library that simplifies and accelerates the development, customization, and comparison of reinforcement learning agents, making DRL research and application more efficient.

Contribution

RLzoo introduces high-level APIs, a model zoo, and an automatic agent construction algorithm to improve DRL development efficiency and flexibility.

Findings

Reduces development time for DRL agents

Achieves comparable performance to existing libraries

Facilitates easy comparison of different DRL models

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.