--- library_name: hivex original_train_name: AerialWildfireSuppression_difficulty_8_task_6_run_id_1_train tags: - hivex - hivex-aerial-wildfire-suppression - reinforcement-learning - multi-agent-reinforcement-learning model-index: - name: hivex-AWS-PPO-baseline-task-6-difficulty-8 results: - task: type: sub-task name: drop_water task-id: 6 difficulty-id: 8 dataset: name: hivex-aerial-wildfire-suppression type: hivex-aerial-wildfire-suppression metrics: - type: crash_count value: 0.010958191571990027 +/- 0.004573914836666436 name: Crash Count verified: true - type: extinguishing_trees value: 0.18876909412210807 +/- 0.14536552612874615 name: Extinguishing Trees verified: true - type: extinguishing_trees_reward value: 0.9438454601913691 +/- 0.7268276219188166 name: Extinguishing Trees Reward verified: true - type: preparing_trees value: 295.6805847167969 +/- 8.689367684892192 name: Preparing Trees verified: true - type: preparing_trees_reward value: 295.6805847167969 +/- 8.689367684892192 name: Preparing Trees Reward verified: true - type: water_drop value: 0.9887655645608902 +/- 0.004140661152640941 name: Water Drop verified: true - type: water_pickup value: 0.0006430462468415499 +/- 0.0013853557027389734 name: Water Pickup verified: true - type: cumulative_reward value: 295.6266799926758 +/- 9.439199947060839 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 8 using the Proximal Policy Optimization (PPO) algorithm.

Environment: **Aerial Wildfire Suppression**
Task: 6
Difficulty: 8
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)