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---
library_name: hivex
original_train_name: DroneBasedReforestation_difficulty_3_task_4_run_id_1_train
tags:
- hivex
- hivex-drone-based-reforestation
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-DBR-PPO-baseline-task-4-difficulty-3
results:
- task:
type: sub-task
name: find_highest_potential_seed_drop_location
task-id: 4
difficulty-id: 3
dataset:
name: hivex-drone-based-reforestation
type: hivex-drone-based-reforestation
metrics:
- type: highest_potential_soild_found
value: 0.9668273150920867 +/- 0.04509604063788774
name: Highest Potential Soild Found
verified: true
- type: out_of_energy_count
value: 0.6642222416400909 +/- 0.0670919881726494
name: Out of Energy Count
verified: true
- type: cumulative_reward
value: -0.030067254307523397 +/- 0.03138709363031481
name: Cumulative Reward
verified: true
---
This model serves as the baseline for the **Drone-Based Reforestation** environment, trained and tested on task <code>4</code> with difficulty <code>3</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>Environment: **Drone-Based Reforestation**<br>Task: <code>4</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) |