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a/docs/environments/all-environments.md +++ b/docs/environments/all-environments.md @@ -7,25 +7,26 @@ title: "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`](https://mo-gymnasium.farama.org/environments/deep-sea-treasure/)
    | 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](https://arxiv.org/pdf/1908.08342.pdf). | -| [`deep-sea-treasure-concave-v0`](https://mo-gymnasium.farama.org/environments/deep-sea-treasure-concave/)
    | 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](https://link.springer.com/article/10.1007/s10994-010-5232-5). | -| [`resource-gathering-v0`](https://mo-gymnasium.farama.org/environments/resource-gathering/)
    | Discrete / Discrete | `[enemy, gold, gem]` | Agent must collect gold or gem. Enemies have a 10% chance of killing the agent. From [Barret & Narayanan 2008](https://dl.acm.org/doi/10.1145/1390156.1390162). | -| [`fishwood-v0`](https://mo-gymnasium.farama.org/environments/fishwood/)
    | 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](https://www.researchgate.net/publication/328718263_Multi-objective_Reinforcement_Learning_for_the_Expected_Utility_of_the_Return). | -| [`breakable-bottles-v0`](https://mo-gymnasium.farama.org/environments/breakable-bottles/)
    | 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](https://www.sciencedirect.com/science/article/pii/S0952197621000336). | -| [`fruit-tree-v0`](https://mo-gymnasium.farama.org/environments/fruit-tree/)
    | 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](https://arxiv.org/pdf/1908.08342.pdf). | -| [`water-reservoir-v0`](https://mo-gymnasium.farama.org/environments/water-reservoir/)
    | 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](https://iwaponline.com/jh/article/15/2/258/3425/Tree-based-fitted-Q-iteration-for-multi-objective). | -| [`four-room-v0`](https://mo-gymnasium.farama.org/environments/four-room/)
    | 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](https://proceedings.mlr.press/v162/alegre22a.html). | -| [`mo-mountaincar-v0`](https://mo-gymnasium.farama.org/environments/mo-mountaincar/)
    | 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](https://www.researchgate.net/publication/220343783_Empirical_evaluation_methods_for_multiobjective_reinforcement_learning_algorithms). | -| [`mo-mountaincarcontinuous-v0`](https://mo-gymnasium.farama.org/environments/mo-mountaincarcontinuous/)
    | Continuous / Continuous | `[time_penalty, fuel_consumption_penalty]` | Continuous Mountain Car env, but with penalties for fuel consumption. | -| [`mo-lunar-lander-v2`](https://mo-gymnasium.farama.org/environments/mo-lunar-lander/)
    | Continuous / Discrete or Continuous | `[landed, shaped_reward, main_engine_fuel, side_engine_fuel]` | MO version of the `LunarLander-v2` [environment](https://gymnasium.farama.org/environments/box2d/lunar_lander/). Objectives defined similarly as in [Hung et al. 2022](https://openreview.net/forum?id=AwWaBXLIJE). | -| [`minecart-v0`](https://mo-gymnasium.farama.org/environments/minecart/)
    | Continuous or Image / Discrete | `[ore1, ore2, fuel]` | Agent must collect two types of ores and minimize fuel consumption. From [Abels et al. 2019](https://arxiv.org/abs/1809.07803v2). | -| [`mo-highway-v0`](https://mo-gymnasium.farama.org/environments/mo-highway/) 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](https://github.com/eleurent/highway-env). | -| [`mo-supermario-v0`](https://mo-gymnasium.farama.org/environments/mo-supermario/)
    | Image / Discrete | `[x_pos, time, death, coin, enemy]` | [:warning: SuperMarioBrosEnv support is limited.] Multi-objective version of [SuperMarioBrosEnv](https://github.com/Kautenja/gym-super-mario-bros). Objectives are defined similarly as in [Yang et al. 2019](https://arxiv.org/pdf/1908.08342.pdf). | -| [`mo-reacher-v4`](https://mo-gymnasium.farama.org/environments/mo-reacher/)
    | Continuous / Discrete | `[target_1, target_2, target_3, target_4]` | Mujoco version of `mo-reacher-v0`, based on `Reacher-v4` [environment](https://gymnasium.farama.org/environments/mujoco/reacher/). | -| [`mo-hopper-v4`](https://mo-gymnasium.farama.org/environments/mo-hopper/)
    | Continuous / Continuous | `[velocity, height, energy]` | Multi-objective version of [Hopper-v4](https://gymnasium.farama.org/environments/mujoco/hopper/) env. | -| [`mo-halfcheetah-v4`](https://mo-gymnasium.farama.org/environments/mo-halfcheetah/)
    | Continuous / Continuous | `[velocity, energy]` | Multi-objective version of [HalfCheetah-v4](https://gymnasium.farama.org/environments/mujoco/half_cheetah/) env. Similar to [Xu et al. 2020](https://github.com/mit-gfx/PGMORL). | +| Env | Obs/Action spaces | Objectives | Description | +|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------|---------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| [`deep-sea-treasure-v0`](https://mo-gymnasium.farama.org/environments/deep-sea-treasure/)
    | 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](https://arxiv.org/pdf/1908.08342.pdf). | +| [`deep-sea-treasure-concave-v0`](https://mo-gymnasium.farama.org/environments/deep-sea-treasure-concave/)
    | 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](https://link.springer.com/article/10.1007/s10994-010-5232-5). | +| [`deep-sea-treasure-mirrored-v0`](https://mo-gymnasium.farama.org/environments/deep-sea-treasure-mirrored/)
    | Discrete / Discrete | `[treasure, time_penalty]` | Harder version of the concave DST [Felten et al. 2022](https://www.scitepress.org/Papers/2022/109891/109891.pdf). | +| [`resource-gathering-v0`](https://mo-gymnasium.farama.org/environments/resource-gathering/)
    | Discrete / Discrete | `[enemy, gold, gem]` | Agent must collect gold or gem. Enemies have a 10% chance of killing the agent. From [Barret & Narayanan 2008](https://dl.acm.org/doi/10.1145/1390156.1390162). | +| [`fishwood-v0`](https://mo-gymnasium.farama.org/environments/fishwood/)
    | 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](https://www.researchgate.net/publication/328718263_Multi-objective_Reinforcement_Learning_for_the_Expected_Utility_of_the_Return). | +| [`breakable-bottles-v0`](https://mo-gymnasium.farama.org/environments/breakable-bottles/)
    | 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](https://www.sciencedirect.com/science/article/pii/S0952197621000336). | +| [`fruit-tree-v0`](https://mo-gymnasium.farama.org/environments/fruit-tree/)
    | 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](https://arxiv.org/pdf/1908.08342.pdf). | +| [`water-reservoir-v0`](https://mo-gymnasium.farama.org/environments/water-reservoir/)
    | 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](https://iwaponline.com/jh/article/15/2/258/3425/Tree-based-fitted-Q-iteration-for-multi-objective). | +| [`four-room-v0`](https://mo-gymnasium.farama.org/environments/four-room/)
    | 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](https://proceedings.mlr.press/v162/alegre22a.html). | +| [`mo-mountaincar-v0`](https://mo-gymnasium.farama.org/environments/mo-mountaincar/)
    | 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](https://www.researchgate.net/publication/220343783_Empirical_evaluation_methods_for_multiobjective_reinforcement_learning_algorithms). | +| [`mo-mountaincarcontinuous-v0`](https://mo-gymnasium.farama.org/environments/mo-mountaincarcontinuous/)
    | Continuous / Continuous | `[time_penalty, fuel_consumption_penalty]` | Continuous Mountain Car env, but with penalties for fuel consumption. | +| [`mo-lunar-lander-v2`](https://mo-gymnasium.farama.org/environments/mo-lunar-lander/)
    | Continuous / Discrete or Continuous | `[landed, shaped_reward, main_engine_fuel, side_engine_fuel]` | MO version of the `LunarLander-v2` [environment](https://gymnasium.farama.org/environments/box2d/lunar_lander/). Objectives defined similarly as in [Hung et al. 2022](https://openreview.net/forum?id=AwWaBXLIJE). | +| [`minecart-v0`](https://mo-gymnasium.farama.org/environments/minecart/)
    | Continuous or Image / Discrete | `[ore1, ore2, fuel]` | Agent must collect two types of ores and minimize fuel consumption. From [Abels et al. 2019](https://arxiv.org/abs/1809.07803v2). | +| [`mo-highway-v0`](https://mo-gymnasium.farama.org/environments/mo-highway/) 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](https://github.com/eleurent/highway-env). | +| [`mo-supermario-v0`](https://mo-gymnasium.farama.org/environments/mo-supermario/)
    | Image / Discrete | `[x_pos, time, death, coin, enemy]` | [:warning: SuperMarioBrosEnv support is limited.] Multi-objective version of [SuperMarioBrosEnv](https://github.com/Kautenja/gym-super-mario-bros). Objectives are defined similarly as in [Yang et al. 2019](https://arxiv.org/pdf/1908.08342.pdf). | +| [`mo-reacher-v4`](https://mo-gymnasium.farama.org/environments/mo-reacher/)
    | Continuous / Discrete | `[target_1, target_2, target_3, target_4]` | Mujoco version of `mo-reacher-v0`, based on `Reacher-v4` [environment](https://gymnasium.farama.org/environments/mujoco/reacher/). | +| [`mo-hopper-v4`](https://mo-gymnasium.farama.org/environments/mo-hopper/)
    | Continuous / Continuous | `[velocity, height, energy]` | Multi-objective version of [Hopper-v4](https://gymnasium.farama.org/environments/mujoco/hopper/) env. | +| [`mo-halfcheetah-v4`](https://mo-gymnasium.farama.org/environments/mo-halfcheetah/)
    | Continuous / Continuous | `[velocity, energy]` | Multi-objective version of [HalfCheetah-v4](https://gymnasium.farama.org/environments/mujoco/half_cheetah/) env. Similar to [Xu et al. 2020](https://github.com/mit-gfx/PGMORL). | ```{toctree} diff --git a/mo_gymnasium/envs/deep_sea_treasure/deep_sea_treasure.py b/mo_gymnasium/envs/deep_sea_treasure/deep_sea_treasure.py index 561c5807..374c15a0 100644 --- a/mo_gymnasium/envs/deep_sea_treasure/deep_sea_treasure.py +++ b/mo_gymnasium/envs/deep_sea_treasure/deep_sea_treasure.py @@ -143,7 +143,6 @@ def __init__(self, render_mode: Optional[str] = None, dst_map=DEFAULT_MAP, float self.map_name = "mirrored" else: raise ValueError("Invalid map") - print(f"Using {self.map_name} map") self._pareto_front = CONVEX_FRONT if self.map_name == "convex" else CONCAVE_FRONT self.dir = {

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