---
library_name: hivex
original_train_name: AerialWildfireSuppression_difficulty_1_task_2_run_id_1_train
tags:
- hivex
- hivex-aerial-wildfire-suppression
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-AWS-PPO-baseline-task-2-difficulty-1
  results:
  - task:
      type: sub-task
      name: maximize_preparing_non_burning_trees
      task-id: 2
      difficulty-id: 1
    dataset:
      name: hivex-aerial-wildfire-suppression
      type: hivex-aerial-wildfire-suppression
    metrics:
    - type: crash_count
      value: 0.10833333656191826 +/- 0.13545462085140364
      name: Crash Count
      verified: true
    - type: extinguishing_trees
      value: 12.764999979734421 +/- 20.904079059070074
      name: Extinguishing Trees
      verified: true
    - type: extinguishing_trees_reward
      value: 63.82500011920929 +/- 104.52039570112963
      name: Extinguishing Trees Reward
      verified: true
    - type: fire_out
      value: 0.46333333626389506 +/- 0.3753984235292453
      name: Fire Out
      verified: true
    - type: fire_too_close_to_city
      value: 0.7550000011920929 +/- 0.37763111996880777
      name: Fire too Close to City
      verified: true
    - type: preparing_trees
      value: 561.5350036621094 +/- 325.15177018123586
      name: Preparing Trees
      verified: true
    - type: preparing_trees_reward
      value: 2807.674984741211 +/- 1625.7588563088673
      name: Preparing Trees Reward
      verified: true
    - type: water_drop
      value: 19.77666656970978 +/- 9.682256516548964
      name: Water Drop
      verified: true
    - type: water_pickup
      value: 19.191666650772095 +/- 9.573244118078943
      name: Water Pickup
      verified: true
    - type: cumulative_reward
      value: 3287.165838623047 +/- 1376.9107350360173
      name: Cumulative Reward
      verified: true
---

This model serves as the baseline for the **Aerial Wildfire Suppression** environment, trained and tested on task <code>2</code> with difficulty <code>1</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>

Environment: **Aerial Wildfire Suppression**<br>
Task: <code>2</code><br>
Difficulty: <code>1</code><br>
Algorithm: <code>PPO</code><br>
Episode Length: <code>3000</code><br>
Training <code>max_steps</code>: <code>1800000</code><br>
Testing <code>max_steps</code>: <code>180000</code><br><br>

Train & Test [Scripts](https://github.com/hivex-research/hivex)<br>
Download the [Environment](https://github.com/hivex-research/hivex-environments)