Strengthening Cooperative Consensus in Multi-Robot Confrontation

Abstract

Multi-agent reinforcement learning (MARL) has proven effective in training multi-robot confrontation, such as StarCraft and robot soccer games. However, the current joint action policies utilized in MARL have been unsuccessful in recognizing and preventing actions that often lead to failures on our side. This exacerbates the cooperation dilemma, ultimately resulting in our agents acting independently and being defeated individually by their opponents. To tackle this challenge, we propose a novel joint action policy, referred to as the consensus action policy (CAP). Specifically, CAP records the number of times each joint action has caused our side to fail in the past and computes a cooperation tendency, which is integrated with each agent’s Q-value and Nash bargaining solution to determine a joint action. The cooperation tendency promotes team cooperation by selecting joint actions that have a high tendency of cooperation and avoiding actions that may lead to team failure. Moreover, the proposed CAP policy can be extended to partially observable scenarios by combining it with Deep Q network (DQN) or actor-critic-based methods. We conducted extensive experiments to compare the proposed method with seven existing joint action policies, including four commonly used methods and three state-of-the-art (SOTA) methods, in terms of episode rewards, winning rates, and other metrics. Our results demonstrate that this approach holds great promise for multi-robot confrontation scenarios.