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--- |
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library_name: hivex |
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original_train_name: WindFarmControl_pattern_4_task_1_run_id_1_train |
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tags: |
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- hivex |
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- hivex-wind-farm-control |
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- reinforcement-learning |
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- multi-agent-reinforcement-learning |
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model-index: |
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- name: hivex-WFC-PPO-baseline-task-1-pattern-4 |
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results: |
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- task: |
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type: sub-task |
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name: avoid_damage |
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task-id: 1 |
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pattern-id: 4 |
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dataset: |
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name: hivex-wind-farm-control |
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type: hivex-wind-farm-control |
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metrics: |
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- type: cumulative_reward |
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value: 4816.654519042969 +/- 48.309486675816395 |
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name: Cumulative Reward |
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verified: true |
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- type: avoid_damage_reward |
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value: 4816.70017578125 +/- 50.83180378290865 |
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name: Avoid Damage Reward |
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verified: true |
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- type: individual_performance |
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value: 0.0 +/- 0.0 |
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name: Individual Performance |
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verified: true |
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--- |
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This model serves as the baseline for the **Wind Farm Control** environment, trained and tested on task <code>1</code> with pattern <code>4</code> using the Proximal Policy Optimization (PPO) algorithm.<br> |
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<br> |
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Environment: **Wind Farm Control**<br> |
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Task: <code>1</code><br> |
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Pattern: <code>4</code><br> |
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Algorithm: <code>PPO</code><br> |
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Episode Length: <code>5000</code><br> |
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Training <code>max_steps</code>: <code>8000000</code><br> |
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Testing <code>max_steps</code>: <code>8000000</code><br> |
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<br> |
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Train & Test [Scripts](https://github.com/hivex-research/hivex)<br> |
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Download the [Environment](https://github.com/hivex-research/hivex-environments) |
