--- library_name: hivex original_train_name: AerialWildfireSuppression_difficulty_3_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-3 results: - task: type: sub-task name: drop_water task-id: 6 difficulty-id: 3 dataset: name: hivex-aerial-wildfire-suppression type: hivex-aerial-wildfire-suppression metrics: - type: crash_count value: 0.01590130275581032 +/- 0.009051027459662414 name: Crash Count verified: true - type: extinguishing_trees value: 0.2118260153569281 +/- 0.3283880873127659 name: Extinguishing Trees verified: true - type: extinguishing_trees_reward value: 1.0591301042586565 +/- 1.641940515491148 name: Extinguishing Trees Reward verified: true - type: preparing_trees value: 234.83799896240234 +/- 10.68919451687246 name: Preparing Trees verified: true - type: preparing_trees_reward value: 234.83799896240234 +/- 10.68919451687246 name: Preparing Trees Reward verified: true - type: water_drop value: 0.9835368990898132 +/- 0.009798197368354513 name: Water Drop verified: true - type: water_pickup value: 0.000875470065511763 +/- 0.001850762177686202 name: Water Pickup verified: true - type: cumulative_reward value: 234.31187438964844 +/- 9.555234680707661 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 3 using the Proximal Policy Optimization (PPO) algorithm.

Environment: **Aerial Wildfire Suppression**
Task: 6
Difficulty: 3
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)