MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent
  Reinforcement Learning
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.
66a11c4a858a536116ce72a0
Florian Felten
claimed_verified
2024-07-25T07:38:51.112Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/n7acw7RTyNg60mh_6NArf.jpeg
Florian Felten
user
ffelten
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Umut Ucak
admin_assigned
2024-07-25T09:09:22.953Z
62a9f65e88e6c493e7e10dcd
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Umut Ucak
user
jetimam
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Hicham Azmani
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2024-07-25T09:09:28.211Z
660d451c4213384002f682c1
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HIcham Azmani
user
hiazmani
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Gao Peng
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Willem Röpke
admin_assigned
2024-07-25T09:09:55.633Z
660fc1f70b43a105cc8851ad
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/AOShAKzEfUoLADqO5JJME.png
Willem Röpke
user
WillemRopke
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Hendrik Baier
66a11c4a858a536116ce72a6
Patrick Mannion
admin_assigned
2024-07-25T09:10:09.636Z
64a17b672597e760a71d555c
/avatars/9668806dfd7c3430a37575bf7a3346b2.svg
Patrick Mannion
user
pmannion
66a11c4a858a536116ce72a7
Diederik M. Roijers
66a11c4a858a536116ce72a8
Jordan K. Terry
66a11c4a858a536116ce72a9
El-Ghazali Talbi
66a11c4a858a536116ce72aa
Grégoire Danoy
66a11c4a858a536116ce72ab
Ann Nowé
66a11c4a858a536116ce72ac
Roxana Rădulescu
claimed_verified
2024-07-25T07:38:48.878Z
66a132d5e5b6ab831c8700a4
https://cdn-avatars.huggingface.co/v1/production/uploads/66a132d5e5b6ab831c8700a4/Fw2aJx5JxHf1f5dDL2z_g.jpeg
Roxana Rădulescu
user
rradules
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent
  Reinforcement Learning
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.
MOMAland introduces standardized environments for multi-objective multi-agent reinforcement learning, offering diverse settings and benchmarking algorithms.
multi-objective multi-agent reinforcement learning MOMARL reinforcement learning RL multi-agent RL single-agent RL MO-Gymnasium Gymnasium PettingZoo state representations reward structures utility considerations