---
library_name: hivex
original_train_name: AerialWildfireSuppression_difficulty_2_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-2
  results:
  - task:
      type: sub-task
      name: maximize_extinguished_burning_trees
      task-id: 1
      difficulty-id: 2
    dataset:
      name: hivex-aerial-wildfire-suppression
      type: hivex-aerial-wildfire-suppression
    metrics:
    - type: crash_count
      value: 0.5083333425223827 +/- 0.2699330882907145
      name: Crash Count
      verified: true
    - type: extinguishing_trees
      value: 4.355555608123541 +/- 6.907337205240576
      name: Extinguishing Trees
      verified: true
    - type: extinguishing_trees_reward
      value: 217.77777848243713 +/- 345.36685865656506
      name: Extinguishing Trees Reward
      verified: true
    - type: fire_out
      value: 0.03333333432674408 +/- 0.08719139656193886
      name: Fire Out
      verified: true
    - type: fire_too_close_to_city
      value: 0.5000000014901161 +/- 0.38616422218364055
      name: Fire too Close to City
      verified: true
    - type: preparing_trees
      value: 390.2111080169678 +/- 226.11041224327414
      name: Preparing Trees
      verified: true
    - type: preparing_trees_reward
      value: 390.2111080169678 +/- 226.11041224327414
      name: Preparing Trees Reward
      verified: true
    - type: water_drop
      value: 6.283333307504654 +/- 3.095559538761643
      name: Water Drop
      verified: true
    - type: water_pickup
      value: 5.966666650772095 +/- 3.12197354808832
      name: Water Pickup
      verified: true
    - type: cumulative_reward
      value: 546.4062370300293 +/- 319.2706067457673
      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>2</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>

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