--- library_name: hivex original_train_name: AerialWildfireSuppression_difficulty_9_task_6_run_id_2_train tags: - hivex - hivex-aerial-wildfire-suppression - reinforcement-learning - multi-agent-reinforcement-learning model-index: - name: hivex-AWS-PPO-baseline-task-6-difficulty-9 results: - task: type: sub-task name: drop_water task-id: 6 difficulty-id: 9 dataset: name: hivex-aerial-wildfire-suppression type: hivex-aerial-wildfire-suppression metrics: - type: crash_count value: 0.01373663340928033 +/- 0.006368126725441811 name: Crash Count verified: true - type: extinguishing_trees value: 0.20510363813955337 +/- 0.13931107935070292 name: Extinguishing Trees verified: true - type: extinguishing_trees_reward value: 1.0255182035267354 +/- 0.6965553894409803 name: Extinguishing Trees Reward verified: true - type: preparing_trees value: 282.37622985839846 +/- 6.297463507454497 name: Preparing Trees verified: true - type: preparing_trees_reward value: 282.37622985839846 +/- 6.297463507454497 name: Preparing Trees Reward verified: true - type: water_drop value: 0.9859137117862702 +/- 0.006026457217121745 name: Water Drop verified: true - type: water_pickup value: 0.0010042423149570824 +/- 0.0014968736951810181 name: Water Pickup verified: true - type: cumulative_reward value: 282.1384475708008 +/- 6.797800299539885 name: Cumulative Reward verified: true --- This model serves as the baseline for the **Aerial Wildfire Suppression** environment, trained and tested on task 6 with difficulty 9 using the Proximal Policy Optimization (PPO) algorithm.

Environment: **Aerial Wildfire Suppression**
Task: 6
Difficulty: 9
Algorithm: PPO
Episode Length: 3000
Training max_steps: 1800000
Testing max_steps: 180000

Train & Test [Scripts](https://github.com/hivex-research/hivex)
Download the [Environment](https://github.com/hivex-research/hivex-environments)