---
library_name: hivex
original_train_name: AerialWildfireSuppression_difficulty_7_task_4_run_id_0_train
tags:
- hivex
- hivex-aerial-wildfire-suppression
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-AWS-PPO-baseline-task-4-difficulty-7
  results:
  - task:
      type: sub-task
      name: protect_village
      task-id: 4
      difficulty-id: 7
    dataset:
      name: hivex-aerial-wildfire-suppression
      type: hivex-aerial-wildfire-suppression
    metrics:
    - type: crash_count
      value: 0.996428570151329 +/- 0.015971919837000092
      name: Crash Count
      verified: true
    - type: extinguishing_trees
      value: 0.5590403918176889 +/- 2.1770827274580227
      name: Extinguishing Trees
      verified: true
    - type: extinguishing_trees_reward
      value: 2.7952019572257996 +/- 10.88541363667466
      name: Extinguishing Trees Reward
      verified: true
    - type: fire_too_close_to_city
      value: 0.010128205269575119 +/- 0.031176732946782266
      name: Fire too Close to City
      verified: true
    - type: preparing_trees
      value: 289.3235092163086 +/- 35.916303930832015
      name: Preparing Trees
      verified: true
    - type: preparing_trees_reward
      value: 289.3235092163086 +/- 35.916303930832015
      name: Preparing Trees Reward
      verified: true
    - type: water_drop
      value: 1.8651554942131043 +/- 0.2548557205509962
      name: Water Drop
      verified: true
    - type: water_pickup
      value: 1.8651554942131043 +/- 0.2548557205509962
      name: Water Pickup
      verified: true
    - type: cumulative_reward
      value: 189.838374710083 +/- 30.620179554518494
      name: Cumulative Reward
      verified: true
---

This model serves as the baseline for the **Aerial Wildfire Suppression** environment, trained and tested on task <code>4</code> with difficulty <code>7</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>

Environment: **Aerial Wildfire Suppression**<br>
Task: <code>4</code><br>
Difficulty: <code>7</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)