Hierarchical and non-hierarchical multi-agent interactions based on unity reinforcement learning
- Publication Type:
- Conference Proceeding
- Citation:
- Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2020, 2020-May, pp. 2095-2097
- Issue Date:
- 2020-01-01
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© 2020 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved. 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.
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