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.

通过辅助学习目标，MAPO-LSO 在多智能体强化学习中通过学习有意义的潜在表示空间来提高样本效率，并展示出在各种多智能体强化学习任务中相比普通的多智能体强化学习模型有显著的学习性能和样本效率的改进。