Benchmarks

In order to ensure consistency of the implementations for various algorithm as the library grows, there are a few automated benchmarks that are ran with every new release.

Following th guidance from Deep Reinforcement Learning that Matters every implementation is tested along various axes of variability:

• Network architecture

• Reward scale

• Random seeds and trials

• Environments