Can waypoints scale MARL for geo-specific terrains?
Abstracting Geo-specific Terrains to Scale Up Reinforcement Learning
This paper explores using waypoints for navigation in multi-agent reinforcement learning (MARL) to reduce computational costs, especially in complex environments like geo-specific terrains used in military simulations. It shows that waypoint-based agents learn faster and perform better than agents using fine-grained continuous movement, even achieving comparable performance to human players in a Counter-Strike scenario. This simplification also enables effective training of agents with differing objectives, a key aspect of realistic simulations. While not directly addressing LLMs, the waypoint approach provides a scalable and efficient training method potentially relevant to future integration with LLM-based agents by reducing the complexity of the action space and accelerating training. The focus on differing objectives also suggests potential applicability to LLM agents with diverse goals and roles.