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---
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library_name: hivex
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original_train_name: AerialWildfireSuppression_difficulty_4_task_6_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-6-difficulty-4
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results:
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- task:
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type: sub-task
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name: drop_water
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task-id: 6
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difficulty-id: 4
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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: 0.019636338157579303 +/- 0.006296536151540525
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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: 0.20098300511017442 +/- 0.20730763026051913
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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: 1.0049150258302688 +/- 1.0365381477211943
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name: Extinguishing Trees Reward
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verified: true
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- type: preparing_trees
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value: 257.4399574279785 +/- 10.902792920729194
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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: 257.4399574279785 +/- 10.902792920729194
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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: 0.9798782348632813 +/- 0.007123928367457922
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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: 0.000187265919521451 +/- 0.0008374786518379387
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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: 256.58973693847656 +/- 11.54790983559329
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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>6</code> with difficulty <code>4</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>6</code><br>
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Difficulty: <code>4</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) |
