---
library_name: hivex
original_train_name: DroneBasedReforestation_difficulty_6_task_1_run_id_1_train
tags:
- hivex
- hivex-drone-based-reforestation
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-DBR-PPO-baseline-task-1-difficulty-6
  results:
  - task:
      type: sub-task
      name: find_closest_forest_perimeter
      task-id: 1
      difficulty-id: 6
    dataset:
      name: hivex-drone-based-reforestation
      type: hivex-drone-based-reforestation
    metrics:
    - type: out_of_energy_count
      value: 0.010789197646081447 +/- 0.016001994100862754
      name: Out of Energy Count
      verified: true
    - type: cumulative_reward
      value: 98.35338226318359 +/- 1.9403345148468187
      name: Cumulative Reward
      verified: true
---

This model serves as the baseline for the **Drone-Based Reforestation** environment, trained and tested on task <code>1</code> with difficulty <code>6</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>Environment: **Drone-Based Reforestation**<br>Task: <code>1</code><br>Difficulty: <code>6</code><br>Algorithm: <code>PPO</code><br>Episode Length: <code>2000</code><br>Training <code>max_steps</code>: <code>1200000</code><br>Testing <code>max_steps</code>: <code>300000</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