  Sample efficiency remains a key challenge in multi-agent reinforcement
learning (MARL). A promising approach is to learn a meaningful latent
representation space through auxiliary learning objectives alongside the MARL
objective to aid in learning a successful control policy. In our work, we
present MAPO-LSO (Multi-Agent Policy Optimization with Latent Space
Optimization) which applies a form of comprehensive representation learning
devised to supplement MARL training. Specifically, MAPO-LSO proposes a
multi-agent extension of transition dynamics reconstruction and self-predictive
learning that constructs a latent state optimization scheme that can be
trivially extended to current state-of-the-art MARL algorithms. Empirical
results demonstrate MAPO-LSO to show notable improvements in sample efficiency
and learning performance compared to its vanilla MARL counterpart without any
additional MARL hyperparameter tuning on a diverse suite of MARL tasks.
