I have spent the last two years learning Reinforcement Learning. I created Marathon Environments to help explore the applicability of robotics and locomotion research to Video Games in the domain of Active Ragdoll and Virtual Agents. This tutorial provides a primer on Marathon Environments. Marathon Environments re-implements the classic set of Continuous Control benchmarks typically seen in Deep Reinforcement Learning literature as Unity environments using the ML-Agents toolkit. Marathon Environments was released alongside Unity ML- Agents v0.5 and includes four continuous control environments.

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.