---
library_name: hivex
original_train_name: DroneBasedReforestation_difficulty_10_task_2_run_id_1_train
tags:
- hivex
- hivex-drone-based-reforestation
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-DBR-PPO-baseline-task-2-difficulty-10
  results:
  - task:
      type: sub-task
      name: pick_up_seed_at_base
      task-id: 2
      difficulty-id: 10
    dataset:
      name: hivex-drone-based-reforestation
      type: hivex-drone-based-reforestation
    metrics:
    - type: out_of_energy_count
      value: 0.5766746163368225 +/- 0.07967204035610403
      name: Out of Energy Count
      verified: true
    - type: recharge_energy_count
      value: 153.73689770169557 +/- 118.94237124542886
      name: Recharge Energy Count
      verified: true
    - type: cumulative_reward
      value: 14.923042809963226 +/- 7.318189161418855
      name: Cumulative Reward
      verified: true
---

This model serves as the baseline for the **Drone-Based Reforestation** environment, trained and tested on task <code>2</code> with difficulty <code>10</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>Environment: **Drone-Based Reforestation**<br>Task: <code>2</code><br>Difficulty: <code>10</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