Efficient Reinforcement Learning Development with RLzoo
  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.
