Wang, Ni, Das, Gautham and Millard, Alan (2022) Learning Cooperative Behaviours in Adversarial Multi-agent Systems. In: Towards Autonomous Robotic Systems, Oxford.
Full content URL: https://doi.org/10.1007/978-3-031-15908-4_15
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| Item Type: | Conference or Workshop contribution (Paper) |
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| Item Status: | Live Archive |
Abstract
This work extends an existing virtual multi-agent platform called RoboSumo to create TripleSumo---a platform for investigating multi-agent cooperative behaviors in continuous action spaces, with physical contact in an adversarial environment. In this paper we investigate a scenario in which two agents, namely `Bug' and `Ant', must team up and push another agent `Spider' out of the arena. To tackle this goal, the newly added agent `Bug' is trained during an ongoing match between `Ant' and `Spider'. `Bug' must develop awareness of the other agents' actions, infer the strategy of both sides, and eventually learn an action policy to cooperate. The reinforcement learning algorithm Deep Deterministic Policy Gradient (DDPG) is implemented with a hybrid reward structure combining dense and sparse rewards. The cooperative behavior is quantitatively evaluated by the mean probability of winning the match and mean number of steps needed to win.
| Keywords: | multi-agent systems, Reinforcement learning, Deep Reinforcement Learning |
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| Subjects: | G Mathematical and Computer Sciences > G760 Machine Learning |
| Divisions: | College of Science > Lincoln Institute for Agri-Food Technology |
| ID Code: | 52230 |
| Deposited On: | 27 Oct 2022 10:35 |
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