Available environments

MO-Gymnasium includes environments taken from the MORL literature, as well as multi-objective version of classical environments, such as Mujoco.

Env	Obs/Action spaces	Objectives	Description
deep-sea-treasure-v0	Discrete / Discrete	[treasure, time_penalty]	Agent is a submarine that must collect a treasure while taking into account a time penalty. Treasures values taken from Yang et al. 2019.
deep-sea-treasure-concave-v0	Discrete / Discrete	[treasure, time_penalty]	Agent is a submarine that must collect a treasure while taking into account a time penalty. Treasures values taken from Vamplew et al. 2010.
resource-gathering-v0	Discrete / Discrete	[enemy, gold, gem]	Agent must collect gold or gem. Enemies have a 10% chance of killing the agent. From Barret & Narayanan 2008.
fishwood-v0	Discrete / Discrete	[fish_amount, wood_amount]	ESR environment, the agent must collect fish and wood to light a fire and eat. From Roijers et al. 2018.
breakable-bottles-v0	Discrete (Dictionary) / Discrete	[time_penalty, bottles_delivered, potential]	Gridworld with 5 cells. The agents must collect bottles from the source location and deliver to the destination. From Vamplew et al. 2021.
fruit-tree-v0	Discrete / Discrete	[nutri1, ..., nutri6]	Full binary tree of depth d=5,6 or 7. Every leaf contains a fruit with a value for the nutrients Protein, Carbs, Fats, Vitamins, Minerals and Water. From Yang et al. 2019.
water-reservoir-v0	Continuous / Continuous	[cost_flooding, deficit_water]	A Water reservoir environment. The agent executes a continuous action, corresponding to the amount of water released by the dam. From Pianosi et al. 2013.
four-room-v0	Discrete / Discrete	[item1, item2, item3]	Agent must collect three different types of items in the map and reach the goal. From Alegre et al. 2022.
mo-mountaincar-v0	Continuous / Discrete	[time_penalty, reverse_penalty, forward_penalty]	Classic Mountain Car env, but with extra penalties for the forward and reverse actions. From Vamplew et al. 2011.
mo-mountaincarcontinuous-v0	Continuous / Continuous	[time_penalty, fuel_consumption_penalty]	Continuous Mountain Car env, but with penalties for fuel consumption.
mo-lunar-lander-v2	Continuous / Discrete or Continuous	[landed, shaped_reward, main_engine_fuel, side_engine_fuel]	MO version of the LunarLander-v2 environment. Objectives defined similarly as in Hung et al. 2022.
minecart-v0	Continuous or Image / Discrete	[ore1, ore2, fuel]	Agent must collect two types of ores and minimize fuel consumption. From Abels et al. 2019.
mo-highway-v0 and mo-highway-fast-v0	Continuous / Discrete	[speed, right_lane, collision]	The agent’s objective is to reach a high speed while avoiding collisions with neighbouring vehicles and staying on the rightest lane. From highway-env.
mo-supermario-v0	Image / Discrete	[x_pos, time, death, coin, enemy]	[:warning: SuperMarioBrosEnv support is limited.] Multi-objective version of SuperMarioBrosEnv. Objectives are defined similarly as in Yang et al. 2019.
mo-reacher-v4	Continuous / Discrete	[target_1, target_2, target_3, target_4]	Mujoco version of mo-reacher-v0, based on Reacher-v4 environment.
mo-hopper-v4	Continuous / Continuous	[velocity, height, energy]	Multi-objective version of Hopper-v4 env.
mo-halfcheetah-v4	Continuous / Continuous	[velocity, energy]	Multi-objective version of HalfCheetah-v4 env. Similar to Xu et al. 2020.