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---
library_name: hivex
original_train_name: AerialWildfireSuppression_difficulty_9_task_5_run_id_2_train
tags:
- hivex
- hivex-aerial-wildfire-suppression
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-AWS-PPO-baseline-task-5-difficulty-9
results:
- task:
type: sub-task
name: pick_up_water
task-id: 5
difficulty-id: 9
dataset:
name: hivex-aerial-wildfire-suppression
type: hivex-aerial-wildfire-suppression
metrics:
- type: water_pickup
value: 0.9976190477609634 +/- 0.010647942115332154
name: Water Pickup
verified: true
- type: cumulative_reward
value: 94.99339561462402 +/- 0.2982787930055043
name: Cumulative Reward
verified: true
---
This model serves as the baseline for the **Aerial Wildfire Suppression** environment, trained and tested on task <code>5</code> with difficulty <code>9</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>
Environment: **Aerial Wildfire Suppression**<br>
Task: <code>5</code><br>
Difficulty: <code>9</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) |