  Reinforcement learning has driven impressive advances in machine learning.
Simultaneously, quantum-enhanced machine learning algorithms using quantum
annealing underlie heavy developments. Recently, a multi-agent reinforcement
learning (MARL) architecture combining both paradigms has been proposed. This
novel algorithm, which utilizes Quantum Boltzmann Machines (QBMs) for Q-value
approximation has outperformed regular deep reinforcement learning in terms of
time-steps needed to converge. However, this algorithm was restricted to
single-agent and small 2x2 multi-agent grid domains. In this work, we propose
an extension to the original concept in order to solve more challenging
problems. Similar to classic DQNs, we add an experience replay buffer and use
different networks for approximating the target and policy values. The
experimental results show that learning becomes more stable and enables agents
to find optimal policies in grid-domains with higher complexity. Additionally,
we assess how parameter sharing influences the agents behavior in multi-agent
domains. Quantum sampling proves to be a promising method for reinforcement
learning tasks, but is currently limited by the QPU size and therefore by the
size of the input and Boltzmann machine.
