metadata
library_name: hivex
original_train_name: DroneBasedReforestation_difficulty_3_task_1_run_id_2_train
tags:
- hivex
- hivex-drone-based-reforestation
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-DBR-PPO-baseline-task-1-difficulty-3
results:
- task:
type: sub-task
name: find_closest_forest_perimeter
task-id: 1
difficulty-id: 3
dataset:
name: hivex-drone-based-reforestation
type: hivex-drone-based-reforestation
metrics:
- type: out_of_energy_count
value: 0.011022608680650591 +/- 0.014198424974033771
name: Out of Energy Count
verified: true
- type: cumulative_reward
value: 98.32546966552735 +/- 1.5560543364802502
name: Cumulative Reward
verified: true
This model serves as the baseline for the Drone-Based Reforestation environment, trained and tested on task 1 with difficulty 3 using the Proximal Policy Optimization (PPO) algorithm.
Environment: Drone-Based Reforestation
Task: 1
Difficulty: 3
Algorithm: PPO
Episode Length: 2000
Training max_steps: 1200000
Testing max_steps: 300000
Train & Test Scripts
Download the Environment