---
library_name: hivex
original_train_name: AerialWildfireSuppression_difficulty_4_task_8_run_id_2_train
tags:
- hivex
- hivex-aerial-wildfire-suppression
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-AWS-PPO-baseline-task-8-difficulty-4
  results:
  - task:
      type: sub-task
      name: find_village
      task-id: 8
      difficulty-id: 4
    dataset:
      name: hivex-aerial-wildfire-suppression
      type: hivex-aerial-wildfire-suppression
    metrics:
    - type: crash_count
      value: 0.07222222425043583 +/- 0.13620208604517808
      name: Crash Count
      verified: true
    - type: cumulative_reward
      value: 21.59722204208374 +/- 40.30039403851721
      name: Cumulative Reward
      verified: true
---

This model serves as the baseline for the **Aerial Wildfire Suppression** environment, trained and tested on task <code>8</code> with difficulty <code>4</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>

Environment: **Aerial Wildfire Suppression**<br>
Task: <code>8</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)