---
library_name: hivex
original_train_name: WindFarmControl_pattern_4_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-4
  results:
  - task:
      type: main-task
      name: main_task
      task-id: 0
      pattern-id: 4
    dataset:
      name: hivex-wind-farm-control
      type: hivex-wind-farm-control
    metrics:
    - type: cumulative_reward
      value: 4613.436940917969 +/- 38.26933135242948
      name: Cumulative Reward
      verified: true
    - type: individual_performance
      value: 4613.370803222656 +/- 38.736289986043545
      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>4</code> using the Proximal Policy Optimization (PPO) algorithm.<br>
<br>
Environment: **Wind Farm Control**<br>
Task: <code>0</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)