  We propose using regularization for Multi-Agent Reinforcement Learning rather
than learning explicit cooperative structures called {\em Multi-Agent
Regularized Q-learning} (MARQ). Many MARL approaches leverage centralized
structures in order to exploit global state information or removing
communication constraints when the agents act in a decentralized manner.
Instead of learning redundant structures which is removed during agent
execution, we propose instead to leverage shared experiences of the agents to
regularize the individual policies in order to promote structured exploration.
We examine several different approaches to how MARQ can either explicitly or
implicitly regularize our policies in a multi-agent setting. MARQ aims to
address these limitations in the MARL context through applying regularization
constraints which can correct bias in off-policy out-of-distribution agent
experiences and promote diverse exploration. Our algorithm is evaluated on
several benchmark multi-agent environments and we show that MARQ consistently
outperforms several baselines and state-of-the-art algorithms; learning in
fewer steps and converging to higher returns.
