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- ---
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- library_name: hivex
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- original_train_name: AerialWildfireSuppression_difficulty_1_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-1
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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: 1
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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: 0.040009206905961034 +/- 0.018735561549171307
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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: 0.4969854736700654 +/- 0.6300676451261423
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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: 2.484927378222346 +/- 3.150338277465211
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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: 143.18572998046875 +/- 12.10208288767324
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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: 143.18572998046875 +/- 12.10208288767324
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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: 0.9589269667863846 +/- 0.018643650861373894
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- name: Water Drop
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- verified: true
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- - type: cumulative_reward
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- value: 141.81843147277831 +/- 13.9801382441756
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- name: Cumulative Reward
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- verified: true
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- ---
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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>1</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>
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-
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- Environment: **Aerial Wildfire Suppression**<br>
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- Task: <code>6</code><br>
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- Difficulty: <code>1</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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-
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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)
 
 
 
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+ ---
2
+ library_name: hivex
3
+ original_train_name: AerialWildfireSuppression_difficulty_1_task_6_run_id_1_train
4
+ tags:
5
+ - hivex
6
+ - hivex-aerial-wildfire-suppression
7
+ - reinforcement-learning
8
+ - multi-agent-reinforcement-learning
9
+ model-index:
10
+ - name: hivex-AWS-PPO-baseline-task-6-difficulty-1
11
+ results:
12
+ - task:
13
+ type: sub-task
14
+ name: drop_water
15
+ task-id: 6
16
+ difficulty-id: 1
17
+ dataset:
18
+ name: hivex-aerial-wildfire-suppression
19
+ type: hivex-aerial-wildfire-suppression
20
+ metrics:
21
+ - type: crash_count
22
+ value: 0.040009206905961034 +/- 0.018735561549171307
23
+ name: Crash Count
24
+ verified: true
25
+ - type: extinguishing_trees
26
+ value: 0.4969854736700654 +/- 0.6300676451261423
27
+ name: Extinguishing Trees
28
+ verified: true
29
+ - type: extinguishing_trees_reward
30
+ value: 2.484927378222346 +/- 3.150338277465211
31
+ name: Extinguishing Trees Reward
32
+ verified: true
33
+ - type: preparing_trees
34
+ value: 143.18572998046875 +/- 12.10208288767324
35
+ name: Preparing Trees
36
+ verified: true
37
+ - type: preparing_trees_reward
38
+ value: 143.18572998046875 +/- 12.10208288767324
39
+ name: Preparing Trees Reward
40
+ verified: true
41
+ - type: water_drop
42
+ value: 0.9589269667863846 +/- 0.018643650861373894
43
+ name: Water Drop
44
+ verified: true
45
+ - type: cumulative_reward
46
+ value: 141.81843147277831 +/- 13.9801382441756
47
+ name: Cumulative Reward
48
+ verified: true
49
+ ---
50
+
51
+ This model serves as the baseline for the **Aerial Wildfire Suppression** environment, trained and tested on task <code>6</code> with difficulty <code>1</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>
52
+
53
+ Environment: **Aerial Wildfire Suppression**<br>
54
+ Task: <code>6</code><br>
55
+ Difficulty: <code>1</code><br>
56
+ Algorithm: <code>PPO</code><br>
57
+ Episode Length: <code>3000</code><br>
58
+ Training <code>max_steps</code>: <code>1800000</code><br>
59
+ Testing <code>max_steps</code>: <code>180000</code><br><br>
60
+
61
+ Train & Test [Scripts](https://github.com/hivex-research/hivex)<br>
62
+ Download the [Environment](https://github.com/hivex-research/hivex-environments)
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+
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+ [hivex-paper]: https://arxiv.org/abs/2501.04180