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hivex-WRM-PPO-baseline-task-0-difficulty-5

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 0 with difficulty 5 using the Proximal Policy Optimization (PPO) algorithm.

Environment: Wildfire Resource Management
Task: 0
Difficulty: 5
Algorithm: PPO
Episode Length: 500
Training max_steps: 450000
Testing max_steps: 45000

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Collection including hivex-research/hivex-WRM-PPO-baseline-task-0-difficulty-5

Evaluation results

  • Cumulative Reward on hivex-wildfire-resource-management
    self-reported
    120.11513977050781 +/- 30.495234755057457
  • Collective Performance on hivex-wildfire-resource-management
    self-reported
    48.047623825073245 +/- 12.30012370845873
  • Individual Performance on hivex-wildfire-resource-management
    self-reported
    25.529859733581542 +/- 6.5168944140036675
  • Reward for Moving Resources to Neighbours on hivex-wildfire-resource-management
    self-reported
    63.44483680725098 +/- 18.86539940904601
  • Reward for Moving Resources to Self on hivex-wildfire-resource-management
    self-reported
    0.6659428238868713 +/- 0.34641883158682535
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