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---
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library_name: hivex
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original_train_name: AerialWildfireSuppression_difficulty_10_task_4_run_id_0_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-4-difficulty-10
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results:
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- task:
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type: sub-task
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name: protect_village
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task-id: 4
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difficulty-id: 10
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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: 0.9949999988079071 +/- 0.022360685106199443
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name: Crash Count
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verified: true
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- type: extinguishing_trees
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value: 0.29754857206717134 +/- 0.6612722591280618
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name: Extinguishing Trees
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verified: true
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- type: extinguishing_trees_reward
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value: 1.4877428144216538 +/- 3.3063611877410133
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name: Extinguishing Trees Reward
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verified: true
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- type: fire_too_close_to_city
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value: 0.022103730216622354 +/- 0.039482863821780616
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name: Fire too Close to City
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verified: true
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- type: preparing_trees
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value: 274.65237579345705 +/- 34.49504850001327
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name: Preparing Trees
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verified: true
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- type: preparing_trees_reward
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value: 274.65237579345705 +/- 34.49504850001327
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name: Preparing Trees Reward
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verified: true
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- type: water_drop
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value: 2.101252889633179 +/- 0.3808157213259622
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name: Water Drop
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verified: true
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- type: water_pickup
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value: 2.101252889633179 +/- 0.3808157213259622
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name: Water Pickup
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verified: true
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- type: cumulative_reward
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value: 168.44546432495116 +/- 38.14918935961842
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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>4</code> with difficulty <code>10</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>4</code><br>
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Difficulty: <code>10</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) |
