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Merge pull request #58 from Farama-Foundation/evaluation_route_choice
Evaluation route choice + renaming references of "congestion" game to route_choice for consistency
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‎docs/environments/all-envs.md

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| [`mo-beach-v0`](https://momaland.farama.org/environments/mobeach/) <br><img src="https://momaland.farama.org/_static/gifs/mobeach.gif" width="400px"> | Any | Discrete / Discrete | `[occupation, mixture]` | Taken from [Mannion_2018](https://www.cambridge.org/core/journals/knowledge-engineering-review/article/reward-shaping-for-knowledgebased-multiobjective-multiagent-reinforcement-learning/75F1507F7CAC7C6625F87AE7CD344D52). MO-Beach is a game with two objectives, reflecting the enjoyment of tourists (agents) on their respective beach sections in terms of crowdedness and diversity of attendees. Each beach section is characterised by a capacity and each agent is characterised by a type. |
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| [`mo-item-gathering-v0`](https://momaland.farama.org/environments/moitem_gathering/) <br><img src="https://momaland.farama.org/_static/gifs/moitem_gathering.gif" width="400px"> | Adversarial | Discrete / Discrete | `[#objects]` (configurable) | Adapted from [Kallstrom_2019](https://www.diva-portal.org/smash/get/diva2:1362933/FULLTEXT01.pdf), is a multi-agent grid world, containing items of different colours. Each colour represents a different objective and the goal of the agents is to collect as many objects as possible. |
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| [`mo-gem-mining-v0`](https://momaland.farama.org/environments/mogem_mining/) <br><img src="https://momaland.farama.org/_static/gifs/mogem_mining.gif" width="400px"> | Cooperative | - / Discrete | `[#gems]` (configurable) | MO version of Gem Mining [Bargiacchi_2018](https://proceedings.mlr.press/v80/bargiacchi18a/bargiacchi18a.pdf). Agents go to different mines to extract different gems (objectives). There are restrictions on which mines can be reached for each agent. Agents also influence each other's producitivity. |
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| [`mo-congestion-v0`](https://momaland.farama.org/environments/mocongestion/) <br><img src="https://momaland.farama.org/_static/gifs/mocongestion.gif" width="400px"> | Adversarial | - / Discrete | `[travel time, cost]` | MO-RouteChoice is a multi-objective extension of the route choice problem [Thomasini_2023](https://alaworkshop2023.github.io/papers/ALA2023_paper_69.pdf), where a number of self-interested drivers (agents) must navigate a road network. |
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| [`mo-route_choice-v0`](https://momaland.farama.org/environments/mo_routechoice/) <br><img src="https://momaland.farama.org/_static/gifs/mo_routechoice.gif" width="400px"> | Adversarial | - / Discrete | `[travel time, cost]` | MO-RouteChoice is a multi-objective extension of the route choice problem [Thomasini_2023](https://alaworkshop2023.github.io/papers/ALA2023_paper_69.pdf), where a number of self-interested drivers (agents) must navigate a road network. |
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| [`mo-pistonball-v0`](https://momaland.farama.org/environments/mopistonball/) <br><img src="https://momaland.farama.org/_static/gifs/mopistonball.gif" width="400px"> | Cooperative | Continuous / Any | `[agent_#n_reward]` (configurable) | An MO version of [PZ's Pistonball](https://pettingzoo.farama.org/environments/butterfly/pistonball/) where the reward of each agent is kept separate. |
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| [`mo-multiwalker-stability-v0`](https://momaland.farama.org/environments/momultiwalker_stabilty/) <br><img src="https://momaland.farama.org/_static/gifs/momultiwalker.gif" width="400px"> | Cooperative | Continuous / Continuous | `[progress right, package stability]` | A MO version of [PZ's MultiWalker](https://pettingzoo.farama.org/environments/sisl/multiwalker/) introduced in [Gupta_2017](https://link.springer.com/chapter/10.1007/978-3-319-71682-4_5), where the agents also seek to keep the package steady. |
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| [`catch-v0`](https://momaland.farama.org/environments/catch/) <br><img src="https://momaland.farama.org/_static/gifs/catch.gif" width="400px"> | Cooperative | Continuous / Continuous | `[distance_target, distance_other_drones]` | Agents must corner and catch a target drone while maintaining distance between themselves. |

‎docs/environments/mocongestion.md

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‎docs/environments/moroute_choice.md

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---
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autogenerated:
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title: MO-RouteChoice
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firstpage:
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---
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# MO-RouteChoice
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| | |
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|---|-----------------------------------|
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| Agents names | `agent_i for i in [0, 4199]` |
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| Action Space | Discrete(3) |
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| Observation Space | Box(0.0, 4200.0, (1,), float32) |
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| Reward Space | Box(-3.0, 0.0, (2,), float32) |
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| Import | `momaland.envs.mo_routechoice_v0` |
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Environment for MO-RouteChoice problem.
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The init method takes in environment arguments and should define the following attributes:
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- possible_agents
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- action_spaces
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- observation_spaces
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These attributes should not be changed after initialization.

‎momaland/envs/congestion/mocongestion_v0.py

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‎momaland/envs/congestion/__init__.py ‎momaland/envs/route_choice/__init__.py

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"""MO Congestion Game.
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"""MO Route Choice Game.
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From De Oliveira Ramos, G., Radulescu, R., Nowe, A., Tavares, A. (2020).
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Toll-Based Learning for Minimising Congestion under Heterogeneous Preferences.

‎momaland/envs/route_choice/moroute_choice_v0.py

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"""Route choice environment for multi-objective optimization."""
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from momaland.envs.route_choice.route_choice import env, parallel_env, raw_env
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__all__ = ["env", "parallel_env", "raw_env"]

‎momaland/envs/congestion/networks/Anaheim.json ‎momaland/envs/route_choice/networks/Anaheim.json

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‎momaland/envs/congestion/networks/BBraess_1_2100_10_c1_2100.json ‎momaland/envs/route_choice/networks/BBraess_1_2100_10_c1_2100.json

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‎momaland/envs/congestion/networks/BBraess_3_2100_10_c1_900.json ‎momaland/envs/route_choice/networks/BBraess_3_2100_10_c1_900.json

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‎momaland/envs/congestion/networks/BBraess_5_2100_10_c1_900.json ‎momaland/envs/route_choice/networks/BBraess_5_2100_10_c1_900.json

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‎momaland/envs/congestion/networks/BBraess_7_2100_10_c1_900.json ‎momaland/envs/route_choice/networks/BBraess_7_2100_10_c1_900.json

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