Cao, Zehong, Kaichiu Wong, Quan Bai, and Chin-Teng Lin. “Hierarchical and non-hierarchical multi-agent interactions based on unity reinforcement learning.” In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS . 2020.
URL1 URL2
The open-source Unity platform, where agents can be trained using hierarchical or non-hierarchical reinforcement learning, supports the use of games and simulations as environments for multiple-agent interactions. In this demonstration, we present hierarchical and non-hierarchical multi-agent interactions based on Unity reinforcement learning, specifically, hierarchical reinforcement learning that sets different levels of agent’s observations to achieve the goal. We created four multi-agent scenarios in the Unity environment, namely, Crawler, Tennis, Banana Collector, and Soccer, to test the interaction performances of hierarchical and non-hierarchical reinforcement learning. The simulation-interaction performances show that hierarchical reinforcement learning can be applied to multi-agent environments and can compete with agents trained via non-hierarchical reinforcement learning.