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hivex-WRM-PPO-baseline-task-2-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 2 with difficulty 5 using the Proximal Policy Optimization (PPO) algorithm.

Environment: Wildfire Resource Management
Task: 2
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-2-difficulty-5

Evaluation results

  • Cumulative Reward on hivex-wildfire-resource-management
    self-reported
    784.7529388427735 +/- 237.5803281220698
  • Collective Performance on hivex-wildfire-resource-management
    self-reported
    46.654307174682614 +/- 12.41762735898119
  • Individual Performance on hivex-wildfire-resource-management
    self-reported
    25.153486728668213 +/- 6.66514805176298
  • Reward for Moving Resources to Neighbours on hivex-wildfire-resource-management
    self-reported
    685.7552093505859 +/- 200.96518835832964
  • Reward for Moving Resources to Self on hivex-wildfire-resource-management
    self-reported
    0.3521461673080921 +/- 0.28661129618806847
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