---
library_name: hivex
original_train_name: WindFarmControl_pattern_4_task_1_run_id_1_train
tags:
- hivex
- hivex-wind-farm-control
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-WFC-PPO-baseline-task-1-pattern-4
  results:
  - task:
      type: sub-task
      name: avoid_damage
      task-id: 1
      pattern-id: 4
    dataset:
      name: hivex-wind-farm-control
      type: hivex-wind-farm-control
    metrics:
    - type: cumulative_reward
      value: 4816.654519042969 +/- 48.309486675816395
      name: Cumulative Reward
      verified: true
    - type: avoid_damage_reward
      value: 4816.70017578125 +/- 50.83180378290865
      name: Avoid Damage Reward
      verified: true
    - type: individual_performance
      value: 0.0 +/- 0.0
      name: Individual Performance
      verified: true
---

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>
<br>
Environment: **Wind Farm Control**<br>
Task: <code>1</code><br>
Pattern: <code>4</code><br>
Algorithm: <code>PPO</code><br>
Episode Length: <code>5000</code><br>
Training <code>max_steps</code>: <code>8000000</code><br>
Testing <code>max_steps</code>: <code>8000000</code><br>
<br>
Train & Test [Scripts](https://github.com/hivex-research/hivex)<br>
Download the [Environment](https://github.com/hivex-research/hivex-environments)

[hivex-paper]: https://arxiv.org/abs/2501.04180