MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent
  Reinforcement Learning
Authors: name: Florian Felten, name: Umut Ucak, name: Hicham Azmani, name: Gao Peng, name: Willem Röpke, name: Hendrik Baier, name: Patrick Mannion, name: Diederik M. Roijers, name: Jordan K. Terry, name: El-Ghazali Talbi, name: Grégoire Danoy, name: Ann Nowé, name: Roxana Rădulescu

Many challenging tasks such as managing traffic systems, electricity grids,
or supply chains involve complex decision-making processes that must balance
multiple conflicting objectives and coordinate the actions of various
independent decision-makers (DMs). One perspective for formalising and
addressing such tasks is multi-objective multi-agent reinforcement learning
(MOMARL). MOMARL broadens reinforcement learning (RL) to problems with multiple
agents each needing to consider multiple objectives in their learning process.
In reinforcement learning research, benchmarks are crucial in facilitating
progress, evaluation, and reproducibility. The significance of benchmarks is
underscored by the existence of numerous benchmark frameworks developed for
various RL paradigms, including single-agent RL (e.g., Gymnasium), multi-agent
RL (e.g., PettingZoo), and single-agent multi-objective RL (e.g.,
MO-Gymnasium). To support the advancement of the MOMARL field, we introduce
MOMAland, the first collection of standardised environments for multi-objective
multi-agent reinforcement learning. MOMAland addresses the need for
comprehensive benchmarking in this emerging field, offering over 10 diverse
environments that vary in the number of agents, state representations, reward
structures, and utility considerations. To provide strong baselines for future
research, MOMAland also includes algorithms capable of learning policies in
such settings.