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Evaluation route choice + renaming references of "congestion" game to route_choice for consistency #58

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2 changes: 1 addition & 1 deletion docs/environments/all-envs.md
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Expand Up @@ -20,7 +20,7 @@ More information are available in the TODO [MOMAland paper]().
| [`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. |
| [`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. |
| [`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. |
| [`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. |
| [`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. |
| [`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. |
| [`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. |
| [`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. |
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23 changes: 0 additions & 23 deletions docs/environments/mocongestion.md

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23 changes: 23 additions & 0 deletions docs/environments/moroute_choice.md
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---
autogenerated:
title: MO-RouteChoice
firstpage:
---

# MO-RouteChoice

| | |
|---|-----------------------------------|
| Agents names | `agent_i for i in [0, 4199]` |
| Action Space | Discrete(3) |
| Observation Space | Box(0.0, 4200.0, (1,), float32) |
| Reward Space | Box(-3.0, 0.0, (2,), float32) |
| Import | `momaland.envs.mo_routechoice_v0` |

Environment for MO-RouteChoice problem.

The init method takes in environment arguments and should define the following attributes:
- possible_agents
- action_spaces
- observation_spaces
These attributes should not be changed after initialization.
5 changes: 0 additions & 5 deletions momaland/envs/congestion/mocongestion_v0.py

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"""MO Congestion Game.
"""MO Route Choice Game.

From De Oliveira Ramos, G., Radulescu, R., Nowe, A., Tavares, A. (2020).
Toll-Based Learning for Minimising Congestion under Heterogeneous Preferences.
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5 changes: 5 additions & 0 deletions momaland/envs/route_choice/moroute_choice_v0.py
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"""Route choice environment for multi-objective optimization."""
from momaland.envs.route_choice.route_choice import env, parallel_env, raw_env


__all__ = ["env", "parallel_env", "raw_env"]
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