Efficient Reinforcement Learning Development with RLzoo

Understand

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.