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---
library_name: hivex
original_train_name: AerialWildfireSuppression_difficulty_6_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-6
results:
- task:
type: sub-task
name: protect_village
task-id: 4
difficulty-id: 6
dataset:
name: hivex-aerial-wildfire-suppression
type: hivex-aerial-wildfire-suppression
metrics:
- type: crash_count
value: 0.995833334326744 +/- 0.01863389536983029
name: Crash Count
verified: true
- type: extinguishing_trees
value: 0.4878607466816902 +/- 1.5825325244644477
name: Extinguishing Trees
verified: true
- type: extinguishing_trees_reward
value: 2.4393037766218186 +/- 7.912662831428387
name: Extinguishing Trees Reward
verified: true
- type: fire_too_close_to_city
value: 0.008012820780277253 +/- 0.024684854855393578
name: Fire too Close to City
verified: true
- type: preparing_trees
value: 277.3211982727051 +/- 28.61713074868279
name: Preparing Trees
verified: true
- type: preparing_trees_reward
value: 277.3211982727051 +/- 28.61713074868279
name: Preparing Trees Reward
verified: true
- type: water_drop
value: 1.76927210688591 +/- 0.30789056520294616
name: Water Drop
verified: true
- type: water_pickup
value: 1.76927210688591 +/- 0.30789056520294616
name: Water Pickup
verified: true
- type: cumulative_reward
value: 177.74845504760742 +/- 32.464829950692376
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>6</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>
Environment: **Aerial Wildfire Suppression**<br>
Task: <code>4</code><br>
Difficulty: <code>6</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)