  Recent advances in deep reinforcement learning in the paradigm of locomotion
using continuous control have raised the interest of game makers for the
potential of digital actors using active ragdoll. Currently, the available
options to develop these ideas are either researchers' limited codebase or
proprietary closed systems. We present Marathon Environments, a suite of open
source, continuous control benchmarks implemented on the Unity game engine,
using the Unity ML- Agents Toolkit. We demonstrate through these benchmarks
that continuous control research is transferable to a commercial game engine.
Furthermore, we exhibit the robustness of these environments by reproducing
advanced continuous control research, such as learning to walk, run and
backflip from motion capture data; learning to navigate complex terrains; and
by implementing a video game input control system. We show further robustness
by training with alternative algorithms found in OpenAI.Baselines. Finally, we
share strategies for significantly reducing the training time.
