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

Understand

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.