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---

library_name: hivex
original_train_name: AerialWildfireSuppression_difficulty_4_task_1_run_id_2_train
tags:
- hivex
- hivex-aerial-wildfire-suppression
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-AWS-PPO-baseline-task-1-difficulty-4
  results:
  - task:
      type: sub-task
      name: maximize_extinguished_burning_trees
      task-id: 1
      difficulty-id: 4
    dataset:
      name: hivex-aerial-wildfire-suppression
      type: hivex-aerial-wildfire-suppression
    metrics:
    - type: crash_count
      value: 0.00833333358168602 +/- 0.03726780073566347
      name: Crash Count
      verified: true
    - type: extinguishing_trees
      value: 15.225000095367431 +/- 25.73818226110129
      name: Extinguishing Trees
      verified: true
    - type: extinguishing_trees_reward
      value: 761.2499908447265 +/- 1286.9091013360119
      name: Extinguishing Trees Reward
      verified: true
    - type: fire_out
      value: 0.36666666716337204 +/- 0.4244018838665434
      name: Fire Out
      verified: true
    - type: fire_too_close_to_city
      value: 0.925 +/- 0.24468024246479642
      name: Fire too Close to City
      verified: true
    - type: preparing_trees
      value: 651.3250035211444 +/- 627.0399351081593
      name: Preparing Trees
      verified: true
    - type: preparing_trees_reward
      value: 651.3250035211444 +/- 627.0399351081593
      name: Preparing Trees Reward
      verified: true
    - type: water_drop
      value: 46.266666889190674 +/- 26.519958404149136
      name: Water Drop
      verified: true
    - type: water_pickup
      value: 45.76666655540466 +/- 26.436529035148055
      name: Water Pickup
      verified: true
    - type: cumulative_reward
      value: 1711.8633205413819 +/- 2423.7230528427467
      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>4</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>

Environment: **Aerial Wildfire Suppression**<br>
Task: <code>1</code><br>
Difficulty: <code>4</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)