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hivex-AWS-PPO-baseline-task-4-difficulty-4

Reinforcement Learning
TensorBoard
ONNX
hivex
hivex-aerial-wildfire-suppression
multi-agent-reinforcement-learning
Eval Results
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This model serves as the baseline for the Aerial Wildfire Suppression environment, trained and tested on task 4 with difficulty 4 using the Proximal Policy Optimization (PPO) algorithm.

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

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

Evaluation results

  • Crash Count on hivex-aerial-wildfire-suppression
    self-reported
    0.995833334326744 +/- 0.01863389536983029
  • Extinguishing Trees on hivex-aerial-wildfire-suppression
    self-reported
    0.0886829849332571 +/- 0.2431451047770778
  • Extinguishing Trees Reward on hivex-aerial-wildfire-suppression
    self-reported
    0.44341491162776947 +/- 1.2157254834823454
  • Fire too Close to City on hivex-aerial-wildfire-suppression
    self-reported
    0.02242424301803112 +/- 0.04967780915490442
  • Preparing Trees on hivex-aerial-wildfire-suppression
    self-reported
    253.21703186035157 +/- 35.590698365756126
  • Preparing Trees Reward on hivex-aerial-wildfire-suppression
    self-reported
    253.21703186035157 +/- 35.590698365756126
  • Water Drop on hivex-aerial-wildfire-suppression
    self-reported
    1.6389704704284669 +/- 0.33632718660720196
  • Water Pickup on hivex-aerial-wildfire-suppression
    self-reported
    1.6389704704284669 +/- 0.33632718660720196
  • Cumulative Reward on hivex-aerial-wildfire-suppression
    self-reported
    145.59471988677979 +/- 48.609159328951485
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