| --- |
| 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 |
