---
library_name: hivex
original_train_name: DroneBasedReforestation_difficulty_1_task_0_run_id_2_train
tags:
- hivex
- hivex-drone-based-reforestation
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-DBR-PPO-baseline-task-0-difficulty-1
results:
- task:
type: main-task
name: main_task
task-id: 0
difficulty-id: 1
dataset:
name: hivex-drone-based-reforestation
type: hivex-drone-based-reforestation
metrics:
- type: cumulative_distance_reward
value: 2.0864201402664184 +/- 0.6296944874797746
name: Cumulative Distance Reward
verified: true
- type: cumulative_distance_until_tree_drop
value: 63.19091514587402 +/- 12.303839558575664
name: Cumulative Distance Until Tree Drop
verified: true
- type: cumulative_distance_to_existing_trees
value: 61.76650863647461 +/- 13.908253887773586
name: Cumulative Distance to Existing Trees
verified: true
- type: cumulative_normalized_distance_until_tree_drop
value: 0.20864201247692107 +/- 0.06296944883377423
name: Cumulative Normalized Distance Until Tree Drop
verified: true
- type: cumulative_tree_drop_reward
value: 5.931592869758606 +/- 1.8518746378631161
name: Cumulative Tree Drop Reward
verified: true
- type: out_of_energy_count
value: 0.9266984140872956 +/- 0.06184757754397895
name: Out of Energy Count
verified: true
- type: recharge_energy_count
value: 10.601777839660645 +/- 1.2478378815502142
name: Recharge Energy Count
verified: true
- type: tree_drop_count
value: 1.0418095350265504 +/- 0.08056789785926544
name: Tree Drop Count
verified: true
- type: cumulative_reward
value: 8.961133165359497 +/- 2.7381643935331064
name: Cumulative Reward
verified: true
---
This model serves as the baseline for the **Drone-Based Reforestation** environment, trained and tested on task 0
with difficulty 1
using the Proximal Policy Optimization (PPO) algorithm.
Environment: **Drone-Based Reforestation**
Task: 0
Difficulty: 1
Algorithm: PPO
Episode Length: 2000
Training max_steps
: 1200000
Testing max_steps
: 300000
Train & Test [Scripts](https://github.com/hivex-research/hivex)
Download the [Environment](https://github.com/hivex-research/hivex-environments)