metadata
library_name: hivex
original_train_name: DroneBasedReforestation_difficulty_4_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-4
results:
- task:
type: sub-task
name: explore_furthest_distance_and_return_to_base
task-id: 6
difficulty-id: 4
dataset:
name: hivex-drone-based-reforestation
type: hivex-drone-based-reforestation
metrics:
- type: furthest_distance_explored
value: 158.60399871826172 +/- 13.606457942958817
name: Furthest Distance Explored
verified: true
- type: out_of_energy_count
value: 0.5861587435007095 +/- 0.07528415521708526
name: Out of Energy Count
verified: true
- type: recharge_energy_count
value: 127.55600050918758 +/- 115.90250626363267
name: Recharge Energy Count
verified: true
- type: cumulative_reward
value: 8.652405815720558 +/- 7.325451196047886
name: Cumulative Reward
verified: true
This model serves as the baseline for the Drone-Based Reforestation environment, trained and tested on task 6 with difficulty 4 using the Proximal Policy Optimization (PPO) algorithm.
Environment: Drone-Based Reforestation
Task: 6
Difficulty: 4
Algorithm: PPO
Episode Length: 2000
Training max_steps: 1200000
Testing max_steps: 300000
Train & Test Scripts
Download the Environment