Learning algorithms

We provide a set of learning algorithms that are compatible with the MOMAland environments. The learning algorithms are implemented in the learning/ directory. To keep everything as self-contained as possible, each algorithm is implemented as a single-file (close to cleanRL’s philosophy).

Nevertheless, we reuse tools provided by other libraries, like multi-objective evaluations and performance indicators from MORL-Baselines.

Here is a list of algorithms that are currently implemented:

Name	Single/Multi-policy	Reward	Utility	Observation space	Action space	Paper
MOMAPPO (OLS) continuous, discrete	Multi	Team	Team / Linear	Any	Any
Scalarized IQL	Single	Individual	Individual / Linear	Discrete	Discrete
Centralization wrapper	Any	Team	Team / Any	Discrete	Discrete
Linearization wrapper	Single	Any	Individual / Linear	Any	Any