Abstracting Geo-specific Terrains to Scale Up Reinforcement Learning

ABSTRACT Multi - agent reinforcement learning (MARL) is increasingly ubiquitous in training dynamic and adaptive synthetic characters for interactive simulations on geo - specific terrains. Frameworks such as Unity's ML - Agents help to make such reinforcement learning e xperiments more accessible to the simulation community. Military training simulations also benefit from advances in MARL, but they have immense computational requirements due to their complex, continuous, stochastic, partially observable, non - stationary, a nd doctrine - based nature. Furthermore, these simulations require geo - specific terrains, further exacerbating the computational resources problem. In our research, we leverage Unity's waypoints to automatically generate multi - layered representation abstract ions of the geo - specific terrains to scale up reinforcement learning while still allowing the transfer of learned policies between different representations. Our early exploratory results on a novel MARL scenario, where each side has differing objectives, indicate that waypoint - based navigation enables faster and more efficient learning while producing trajectories similar to those taken by expert human players in CSGO gaming environments. This research points out the potential of waypoint - based navigation for reducing the computational costs of developing and training MARL models for military training simulations, where geo - specific terrains and differing objectives are crucial. ABOUT THE AUTHORS Volkan Ustun is the Associate Director of the Human - Inspired Adaptive Teaming Systems Group at the USC I nstitute for Creative Technologies .