---
library_name: hivex
original_train_name: AerialWildfireSuppression_difficulty_1_task_6_run_id_1_train
tags:
- hivex
- hivex-aerial-wildfire-suppression
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-AWS-PPO-baseline-task-6-difficulty-1
  results:
  - task:
      type: sub-task
      name: drop_water
      task-id: 6
      difficulty-id: 1
    dataset:
      name: hivex-aerial-wildfire-suppression
      type: hivex-aerial-wildfire-suppression
    metrics:
    - type: crash_count
      value: 0.040009206905961034 +/- 0.018735561549171307
      name: Crash Count
      verified: true
    - type: extinguishing_trees
      value: 0.4969854736700654 +/- 0.6300676451261423
      name: Extinguishing Trees
      verified: true
    - type: extinguishing_trees_reward
      value: 2.484927378222346 +/- 3.150338277465211
      name: Extinguishing Trees Reward
      verified: true
    - type: preparing_trees
      value: 143.18572998046875 +/- 12.10208288767324
      name: Preparing Trees
      verified: true
    - type: preparing_trees_reward
      value: 143.18572998046875 +/- 12.10208288767324
      name: Preparing Trees Reward
      verified: true
    - type: water_drop
      value: 0.9589269667863846 +/- 0.018643650861373894
      name: Water Drop
      verified: true
    - type: cumulative_reward
      value: 141.81843147277831 +/- 13.9801382441756
      name: Cumulative Reward
      verified: true
---

This model serves as the baseline for the **Aerial Wildfire Suppression** environment, trained and tested on task <code>6</code> with difficulty <code>1</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>

Environment: **Aerial Wildfire Suppression**<br>
Task: <code>6</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)