A Biologically Inspired Architecture for Multiagent Games

This paper reports modifications on a biologically inspired robotic architecture originally designed to work in single agent contexts. Several adaptations have been applied to the architecture, seeking as result a model-free artificial agent able to accomplish shared goals in a multiagent environment, from sensorial information translated into homeostatic variable values and a rule database that play roles respectively in temporal credit assignment and action-state space exploration. The new architecture was tested in a well-known benchmark game, and the results were compared to the ones from the multiagent RL algorithm Wolf-PHC. We verified that the proposed architecture can produce coordinated behaviour equivalent to WoLF-PHC in stationary domains, and is also able to learn cooperation in non-stationary domains. The proposal is a first step towards an artificial agent that cooperate as result of a biologically plausible computational model of morality.