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hivex-WRM-PPO-baseline-task-1-difficulty-1

Reinforcement Learning
TensorBoard
ONNX
hivex
hivex-wildfire-resource-management
multi-agent-reinforcement-learning
Eval Results
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This model serves as the baseline for the Wildfire Resource Management environment, trained and tested on task 1 with difficulty 1 using the Proximal Policy Optimization (PPO) algorithm.

Environment: Wildfire Resource Management
Task: 1
Difficulty: 1
Algorithm: PPO
Episode Length: 500
Training max_steps: 450000
Testing max_steps: 45000

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Evaluation results

  • Cumulative Reward on hivex-wildfire-resource-management
    self-reported
    195.4663185119629 +/- 77.65158484674649
  • Collective Performance on hivex-wildfire-resource-management
    self-reported
    34.28780403137207 +/- 10.980421619205345
  • Individual Performance on hivex-wildfire-resource-management
    self-reported
    16.798346424102782 +/- 5.2696319909880085
  • Reward for Moving Resources to Neighbours on hivex-wildfire-resource-management
    self-reported
    1.2929230749607086 +/- 0.5330753884066604
  • Reward for Moving Resources to Self on hivex-wildfire-resource-management
    self-reported
    143.07430686950684 +/- 44.849034918704156
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