--- library_name: hivex original_train_name: AerialWildfireSuppression_difficulty_6_task_7_run_id_2_train tags: - hivex - hivex-aerial-wildfire-suppression - reinforcement-learning - multi-agent-reinforcement-learning model-index: - name: hivex-AWS-PPO-baseline-task-7-difficulty-6 results: - task: type: sub-task name: find_fire task-id: 7 difficulty-id: 6 dataset: name: hivex-aerial-wildfire-suppression type: hivex-aerial-wildfire-suppression metrics: - type: crash_count value: 0.07729871394112706 +/- 0.10564317145739546 name: Crash Count verified: true - type: cumulative_reward value: 73.41971187591552 +/- 27.54676335258844 name: Cumulative Reward verified: true --- This model serves as the baseline for the **Aerial Wildfire Suppression** environment, trained and tested on task <code>7</code> with difficulty <code>6</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br> Environment: **Aerial Wildfire Suppression**<br> Task: <code>7</code><br> Difficulty: <code>6</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)