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Recent advances in deep reinforcement learning in the paradigm of locomotionusing continuous control have raised the interest of game makers for thepotential of digital actors using active ragdoll. Currently, the availableoptions to develop these ideas are either researchers' limited codebase orproprietary closed systems. We present Marathon Environments, a suite of opensource, continuous control benchmarks implemented on the Unity game engine,using the Unity ML- Agents Toolkit. We demonstrate through these benchmarksthat continuous control research is transferable to a commercial game engine.Furthermore, we exhibit the robustness of these environments by reproducingadvanced continuous control research, such as learning to walk, run andbackflip from motion capture data; learning to navigate complex terrains; andby implementing a video game input control system. We show further robustnessby training with alternative algorithms found in OpenAI.Baselines. Finally, weshare strategies for significantly reducing the training time.

Marathon Environments: Multi-Agent Continuous Control Benchmarks in a Modern Video Game Engine