Modularity benefits reinforcement learning agents with competing homeostatic drives

Zack Dulberg, Rachit Dubey, Isabel M. Berwian +1

The problem of balancing conflicting needs is fundamental to intelligence. Standard reinforcement learning algorithms maximize a scalar reward, which requires combining different o…