  The sample-inefficiency problem in Artificial Intelligence refers to the
inability of current Deep Reinforcement Learning models to optimize action
policies within a small number of episodes. Recent studies have tried to
overcome this limitation by adding memory systems and architectural biases to
improve learning speed, such as in Episodic Reinforcement Learning. However,
despite achieving incremental improvements, their performance is still not
comparable to how humans learn behavioral policies. In this paper, we
capitalize on the design principles of the Distributed Adaptive Control (DAC)
theory of mind and brain to build a novel cognitive architecture (DAC-ML) that,
by incorporating a hippocampus-inspired sequential memory system, can rapidly
converge to effective action policies that maximize reward acquisition in a
challenging foraging task.
