This model serves as the baseline for the Aerial Wildfire Suppression environment, trained and tested on task 0 with difficulty 9 using the Proximal Policy Optimization (PPO) algorithm.

Environment: Aerial Wildfire Suppression
Task: 0
Difficulty: 9
Algorithm: PPO
Episode Length: 3000
Training max_steps: 1800000
Testing max_steps: 180000

Train & Test Scripts
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Reinforcement Learning

Evaluation results

Crash Count on hivex-aerial-wildfire-suppression
self-reported

0.24166667386889457 +/- 0.1750104476777611
Extinguishing Trees on hivex-aerial-wildfire-suppression
self-reported

17.141666746139528 +/- 39.513165920891936
Extinguishing Trees Reward on hivex-aerial-wildfire-suppression
self-reported

85.70833311080932 +/- 197.56582920337453
Fire Out on hivex-aerial-wildfire-suppression
self-reported

0.12500000223517418 +/- 0.24106852927463382
Fire too Close to City on hivex-aerial-wildfire-suppression
self-reported

0.9 +/- 0.2615741818902984
Preparing Trees on hivex-aerial-wildfire-suppression
self-reported

721.7666670084 +/- 737.4681856167363
Preparing Trees Reward on hivex-aerial-wildfire-suppression
self-reported

721.7666670084 +/- 737.4681856167363
Water Drop on hivex-aerial-wildfire-suppression
self-reported

66.28333377838135 +/- 30.644495531353783
Water Pickup on hivex-aerial-wildfire-suppression
self-reported

65.93333344459533 +/- 30.521844562150648
Cumulative Reward on hivex-aerial-wildfire-suppression
self-reported

996.1558359742164 +/- 875.8416918638716

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