---
library_name: hivex
original_train_name: AerialWildfireSuppression_difficulty_2_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-2
  results:
  - task:
      type: sub-task
      name: protect_village
      task-id: 4
      difficulty-id: 2
    dataset:
      name: hivex-aerial-wildfire-suppression
      type: hivex-aerial-wildfire-suppression
    metrics:
    - type: crash_count
      value: 0.9954545468091964 +/- 0.020327884646360022
      name: Crash Count
      verified: true
    - type: extinguishing_trees
      value: 0.28887686133384705 +/- 0.6787165224682201
      name: Extinguishing Trees
      verified: true
    - type: extinguishing_trees_reward
      value: 1.4443842768669128 +/- 3.393582550370347
      name: Extinguishing Trees Reward
      verified: true
    - type: fire_too_close_to_city
      value: 0.023333333805203436 +/- 0.052815469164145035
      name: Fire too Close to City
      verified: true
    - type: preparing_trees
      value: 173.5697063446045 +/- 22.599638913107242
      name: Preparing Trees
      verified: true
    - type: preparing_trees_reward
      value: 173.5697063446045 +/- 22.599638913107242
      name: Preparing Trees Reward
      verified: true
    - type: water_drop
      value: 1.2260812371969223 +/- 0.2007275933789739
      name: Water Drop
      verified: true
    - type: water_pickup
      value: 1.2260812371969223 +/- 0.2007275933789739
      name: Water Pickup
      verified: true
    - type: cumulative_reward
      value: 65.80658979415894 +/- 25.50451599293347
      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>2</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>

Environment: **Aerial Wildfire Suppression**<br>
Task: <code>4</code><br>
Difficulty: <code>2</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)