---
library_name: hivex
original_train_name: WindFarmControl_pattern_3_task_0_run_id_2_train
tags:
- hivex
- hivex-wind-farm-control
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-WFC-PPO-baseline-task-0-pattern-3
  results:
  - task:
      type: main-task
      name: main_task
      task-id: 0
      pattern-id: 3
    dataset:
      name: hivex-wind-farm-control
      type: hivex-wind-farm-control
    metrics:
    - type: cumulative_reward
      value: 4601.440402832031 +/- 41.81579798428404
      name: Cumulative Reward
      verified: true
    - type: individual_performance
      value: 4601.383317871094 +/- 41.655823555724716
      name: Individual Performance
      verified: true
---

This model serves as the baseline for the **Wind Farm Control** environment, trained and tested on task <code>0</code> with pattern <code>3</code> using the Proximal Policy Optimization (PPO) algorithm.<br>
<br>
Environment: **Wind Farm Control**<br>
Task: <code>0</code><br>
Pattern: <code>3</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