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MO-Gymnasium 1.0.0 Release Notes

reward = forward_reward - ctrl_cost

With MORL, users have the flexibility to determine the compromises they desire based on their preferences for each objective. Consequently, the environments in MO-Gymnasium do not have predefined weights. Thus, MO-Gymnasium extends the capabilities of Gymnasium to the multi-objective setting, where the agents receives a vectorial reward.

For example, here is an illustration of the multiple policies learned by an MORL agent for the mo-halfcheetah domain, balancing between saving battery and speed:

- +

This release marks the first mature version of MO-Gymnasium within Farama, indicating that the API is stable, and we have achieved a high level of quality in this library.

API