---
library_name: hivex
original_train_name: AerialWildfireSuppression_difficulty_7_task_1_run_id_1_train
tags:
- hivex
- hivex-aerial-wildfire-suppression
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-AWS-PPO-baseline-task-1-difficulty-7
  results:
  - task:
      type: sub-task
      name: maximize_extinguished_burning_trees
      task-id: 1
      difficulty-id: 7
    dataset:
      name: hivex-aerial-wildfire-suppression
      type: hivex-aerial-wildfire-suppression
    metrics:
    - type: crash_count
      value: 0.42777778655290605 +/- 0.26862100733560945
      name: Crash Count
      verified: true
    - type: extinguishing_trees
      value: 33.80000016689301 +/- 38.84839322135217
      name: Extinguishing Trees
      verified: true
    - type: extinguishing_trees_reward
      value: 1690.0000106811524 +/- 1942.4196808320014
      name: Extinguishing Trees Reward
      verified: true
    - type: fire_out
      value: 0.06666666865348816 +/- 0.13679711768822556
      name: Fire Out
      verified: true
    - type: fire_too_close_to_city
      value: 0.9583333343267441 +/- 0.13106625028248262
      name: Fire too Close to City
      verified: true
    - type: preparing_trees
      value: 636.8777788162231 +/- 568.0216883131019
      name: Preparing Trees
      verified: true
    - type: preparing_trees_reward
      value: 636.8777788162231 +/- 568.0216883131019
      name: Preparing Trees Reward
      verified: true
    - type: water_drop
      value: 38.541666984558105 +/- 15.550133835890938
      name: Water Drop
      verified: true
    - type: water_pickup
      value: 38.38333382606506 +/- 15.581525306646949
      name: Water Pickup
      verified: true
    - type: cumulative_reward
      value: 2319.882499694824 +/- 1935.6842099941402
      name: Cumulative Reward
      verified: true
---

This model serves as the baseline for the **Aerial Wildfire Suppression** environment, trained and tested on task <code>1</code> with difficulty <code>7</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>

Environment: **Aerial Wildfire Suppression**<br>
Task: <code>1</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)