| --- |
| library_name: hivex |
| original_train_name: DroneBasedReforestation_difficulty_3_task_6_run_id_1_train |
| tags: |
| - hivex |
| - hivex-drone-based-reforestation |
| - reinforcement-learning |
| - multi-agent-reinforcement-learning |
| model-index: |
| - name: hivex-DBR-PPO-baseline-task-6-difficulty-3 |
| results: |
| - task: |
| type: sub-task |
| name: explore_furthest_distance_and_return_to_base |
| task-id: 6 |
| difficulty-id: 3 |
| dataset: |
| name: hivex-drone-based-reforestation |
| type: hivex-drone-based-reforestation |
| metrics: |
| - type: furthest_distance_explored |
| value: 146.28668838500977 +/- 16.658292228774815 |
| name: Furthest Distance Explored |
| verified: true |
| - type: out_of_energy_count |
| value: 0.595357158780098 +/- 0.08324645242738359 |
| name: Out of Energy Count |
| verified: true |
| - type: recharge_energy_count |
| value: 131.29083390399813 +/- 117.44315350412963 |
| name: Recharge Energy Count |
| verified: true |
| - type: cumulative_reward |
| value: 6.076253048032522 +/- 5.155621265658263 |
| name: Cumulative Reward |
| verified: true |
| --- |
| |
| This model serves as the baseline for the **Drone-Based Reforestation** environment, trained and tested on task <code>6</code> with difficulty <code>3</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>Environment: **Drone-Based Reforestation**<br>Task: <code>6</code><br>Difficulty: <code>3</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 |
