Abstracting Geo-specific Terrains to Scale Up Reinforcement Learning

2025-03-25

AI Summary

In military training simulation, multi-agent reinforcement learning (MARL) over geographically specific terrains faces challenges including high computational overhead and poor policy transferability, exacerbated by partial observability, non-stationarity, and doctrinal constraints. To address these, we propose a Unity waypoint-based multi-level terrain abstraction method—introducing, for the first time, waypoint-driven hierarchical abstraction into MARL frameworks to construct a transferable hierarchical partially observable Markov decision process (POMDP) model. Our approach preserves geographic fidelity while significantly improving computational efficiency and enabling cross-level policy transfer. Experimental results in a two-objective adversarial scenario demonstrate substantially accelerated training convergence, generated agent trajectories closely matching expert behaviors from Counter-Strike: Global Offensive (CSGO), and marked reductions in both GPU resource consumption and total training time.

Technology Category

• Multiagent Systems: Adversarial Agents
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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 experiments 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, and 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 abstractions 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.

Problem

• Scaling MARL for military simulations on geo-specific terrains
• Reducing computational costs in complex MARL environments
• Transferring learned policies across terrain representations

Innovation

• Leverages Unity's waypoints for terrain abstraction
• Generates multi-layered terrain representations
• Enables policy transfer between different representations