Case-Based Goal-Driven Coordination of Multiple Learning Agents

Although several recent studies have been published on goal reasoning (i.e., the study of agents that can self-select their goals), none have focused on the task of learning and acting on large state and action spaces. We introduce GDA-C, a case-based goal reasoning algorithm that divides the state and action space among cooperating learning agents. Cooperation between agents emerges because (1) they share a common reward function and (2) GDA-C formulates the goal that each agent needs to achieve. We claim that its case-based approach for goal formulation is critical to the agents’ performance. To test this claim we conducted an empirical study using the Wargus RTS environment, where we found that GDA-C outperforms its non-GDA ablation.