This model serves as the baseline for the Aerial Wildfire Suppression environment, trained and tested on task 4 with difficulty 9 using the Proximal Policy Optimization (PPO) algorithm.

Environment: Aerial Wildfire Suppression
Task: 4
Difficulty: 9
Algorithm: PPO
Episode Length: 3000
Training max_steps: 1800000
Testing max_steps: 180000

Train & Test Scripts
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Reinforcement Learning

Collection including hivex-research/hivex-AWS-PPO-baseline-task-4-difficulty-9

Aerial Wildfire Suppression

Collection

90 items • Updated Mar 20

Evaluation results

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

0.996428570151329 +/- 0.015971919837000092
Extinguishing Trees on hivex-aerial-wildfire-suppression
self-reported

0.04055555630475283 +/- 0.1275818509161524
Extinguishing Trees Reward on hivex-aerial-wildfire-suppression
self-reported

0.20277777537703515 +/- 0.6379092306314261
Fire too Close to City on hivex-aerial-wildfire-suppression
self-reported

0.004545454680919647 +/- 0.020327891310361893
Preparing Trees on hivex-aerial-wildfire-suppression
self-reported

276.61525497436526 +/- 33.81716366148196
Preparing Trees Reward on hivex-aerial-wildfire-suppression
self-reported

276.61525497436526 +/- 33.81716366148196
Water Drop on hivex-aerial-wildfire-suppression
self-reported

2.0456768214702605 +/- 0.4084652912050666
Water Pickup on hivex-aerial-wildfire-suppression
self-reported

2.0456768214702605 +/- 0.4084652912050666
Cumulative Reward on hivex-aerial-wildfire-suppression
self-reported

177.27129974365235 +/- 36.12212164713132

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