  Developing effective Multi-Agent Systems (MAS) is critical for many
applications requiring collaboration and coordination with humans. Despite the
rapid advance of Multi-Agent Deep Reinforcement Learning (MADRL) in cooperative
MAS, one major challenge is the simultaneous learning and interaction of
independent agents in dynamic environments in the presence of stochastic
rewards. State-of-the-art MADRL models struggle to perform well in Coordinated
Multi-agent Object Transportation Problems (CMOTPs), wherein agents must
coordinate with each other and learn from stochastic rewards. In contrast,
humans often learn rapidly to adapt to nonstationary environments that require
coordination among people. In this paper, motivated by the demonstrated ability
of cognitive models based on Instance-Based Learning Theory (IBLT) to capture
human decisions in many dynamic decision making tasks, we propose three
variants of Multi-Agent IBL models (MAIBL). The idea of these MAIBL algorithms
is to combine the cognitive mechanisms of IBLT and the techniques of MADRL
models to deal with coordination MAS in stochastic environments from the
perspective of independent learners. We demonstrate that the MAIBL models
exhibit faster learning and achieve better coordination in a dynamic CMOTP task
with various settings of stochastic rewards compared to current MADRL models.
We discuss the benefits of integrating cognitive insights into MADRL models.
