---
library_name: hivex
original_train_name: DroneBasedReforestation_difficulty_4_task_3_run_id_2_train
tags:
- hivex
- hivex-drone-based-reforestation
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-DBR-PPO-baseline-task-3-difficulty-4
  results:
  - task:
      type: sub-task
      name: drop_seed
      task-id: 3
      difficulty-id: 4
    dataset:
      name: hivex-drone-based-reforestation
      type: hivex-drone-based-reforestation
    metrics:
    - type: cumulative_distance_reward
      value: 1.3088074851036071 +/- 0.2150446017537585
      name: Cumulative Distance Reward
      verified: true
    - type: cumulative_distance_until_tree_drop
      value: 48.39169616699219 +/- 5.7290635352219415
      name: Cumulative Distance Until Tree Drop
      verified: true
    - type: cumulative_distance_to_existing_trees
      value: 64.92751655578613 +/- 4.971308759365728
      name: Cumulative Distance to Existing Trees
      verified: true
    - type: cumulative_normalized_distance_until_tree_drop
      value: 0.13088074818253517 +/- 0.021504460692074904
      name: Cumulative Normalized Distance Until Tree Drop
      verified: true
    - type: cumulative_tree_drop_reward
      value: 3.975467157363892 +/- 0.5825542394332213
      name: Cumulative Tree Drop Reward
      verified: true
    - type: out_of_energy_count
      value: 0.04072725491598248 +/- 0.02455264640086014
      name: Out of Energy Count
      verified: true
    - type: recharge_energy_count
      value: 10.695373344421387 +/- 0.5710744663818678
      name: Recharge Energy Count
      verified: true
    - type: tree_drop_count
      value: 0.9469023418426513 +/- 0.033324031370099066
      name: Tree Drop Count
      verified: true
    - type: cumulative_reward
      value: 101.1289744567871 +/- 3.8212373566735938
      name: Cumulative Reward
      verified: true
---

This model serves as the baseline for the **Drone-Based Reforestation** environment, trained and tested on task <code>3</code> with difficulty <code>4</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>Environment: **Drone-Based Reforestation**<br>Task: <code>3</code><br>Difficulty: <code>4</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