Learning cooperative behaviours in adversarial multi-agent systems

TL;DR

This paper introduces TripleSumo, an extended multi-agent platform for studying cooperative behaviors in adversarial environments with continuous actions, demonstrating how agents can learn cooperation through reinforcement learning.

Contribution

It extends RoboSumo to TripleSumo, enabling investigation of cooperative behaviors in continuous, contact-rich adversarial settings with a new training scenario.

Findings

Agents can learn effective cooperation strategies.

Hybrid reward structures improve learning efficiency.

Cooperative behaviors increase winning probabilities.

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.