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---
library_name: hivex
original_train_name: WildfireResourceManagement_difficulty_2_task_2_run_id_1_train
tags:
- hivex
- hivex-wildfire-resource-management
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-WRM-PPO-baseline-task-2-difficulty-2
results:
- task:
type: sub-task
name: distribute_all
task-id: 2
difficulty-id: 2
dataset:
name: hivex-wildfire-resource-management
type: hivex-wildfire-resource-management
metrics:
- type: cumulative_reward
value: 719.8077911376953 +/- 176.60168760638808
name: Cumulative Reward
verified: true
- type: collective_performance
value: 49.83914451599121 +/- 17.927922628117184
name: Collective Performance
verified: true
- type: individual_performance
value: 26.82857608795166 +/- 10.644374836699683
name: Individual Performance
verified: true
- type: reward_for_moving_resources_to_neighbours
value: 670.3809020996093 +/- 354.66801295508003
name: Reward for Moving Resources to Neighbours
verified: true
- type: reward_for_moving_resources_to_self
value: 0.3922588244080544 +/- 0.20202251712801628
name: Reward for Moving Resources to Self
verified: true
---
This model serves as the baseline for the **Wildfire Resource Management** environment, trained and tested on task <code>2</code> with difficulty <code>2</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>
Environment: **Wildfire Resource Management**<br>
Task: <code>2</code><br>
Difficulty: <code>2</code><br>
Algorithm: <code>PPO</code><br>
Episode Length: <code>500</code><br>
Training <code>max_steps</code>: <code>450000</code><br>
Testing <code>max_steps</code>: <code>45000</code><br><br>
Train & Test [Scripts](https://github.com/hivex-research/hivex)<br>
Download the [Environment](https://github.com/hivex-research/hivex-environments) |