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---
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library_name: hivex
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original_train_name: AerialWildfireSuppression_difficulty_8_task_8_run_id_1_train
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tags:
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- hivex
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- hivex-aerial-wildfire-suppression
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- reinforcement-learning
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- multi-agent-reinforcement-learning
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model-index:
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- name: hivex-AWS-PPO-baseline-task-8-difficulty-8
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results:
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- task:
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type: sub-task
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name: find_village
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task-id: 8
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difficulty-id: 8
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dataset:
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name: hivex-aerial-wildfire-suppression
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type: hivex-aerial-wildfire-suppression
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metrics:
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- type: crash_count
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value: 0.07833333536982537 +/- 0.12660792045593888
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name: Crash Count
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verified: true
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- type: cumulative_reward
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value: 30.990195941925048 +/- 44.5910033782468
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name: Cumulative Reward
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verified: true
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---
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This model serves as the baseline for the **Aerial Wildfire Suppression** environment, trained and tested on task <code>8</code> with difficulty <code>8</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>
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Environment: **Aerial Wildfire Suppression**<br>
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Task: <code>8</code><br>
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Difficulty: <code>8</code><br>
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Algorithm: <code>PPO</code><br>
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Episode Length: <code>3000</code><br>
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Training <code>max_steps</code>: <code>1800000</code><br>
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Testing <code>max_steps</code>: <code>180000</code><br><br>
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Train & Test [Scripts](https://github.com/hivex-research/hivex)<br>
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Download the [Environment](https://github.com/hivex-research/hivex-environments) |
