--- library_name: hivex original_train_name: AerialWildfireSuppression_difficulty_5_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-5 results: - task: type: sub-task name: drop_water task-id: 6 difficulty-id: 5 dataset: name: hivex-aerial-wildfire-suppression type: hivex-aerial-wildfire-suppression metrics: - type: crash_count value: 0.022751575300935654 +/- 0.009602885208751202 name: Crash Count verified: true - type: extinguishing_trees value: 0.2077373639680445 +/- 0.1507774190085061 name: Extinguishing Trees verified: true - type: extinguishing_trees_reward value: 1.0386868014931678 +/- 0.753887068245034 name: Extinguishing Trees Reward verified: true - type: preparing_trees value: 275.6179458618164 +/- 12.486733727275665 name: Preparing Trees verified: true - type: preparing_trees_reward value: 275.6179458618164 +/- 12.486733727275665 name: Preparing Trees Reward verified: true - type: water_drop value: 0.9767484277486801 +/- 0.00989079886411895 name: Water Drop verified: true - type: water_pickup value: 0.0005922459880821407 +/- 0.0015700193469281448 name: Water Pickup verified: true - type: cumulative_reward value: 274.48731155395507 +/- 13.254462171454286 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 5 using the Proximal Policy Optimization (PPO) algorithm.

Environment: **Aerial Wildfire Suppression**
Task: 6
Difficulty: 5
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)