metadata
library_name: hivex
original_train_name: DroneBasedReforestation_difficulty_2_task_6_run_id_2_train
tags:
- hivex
- hivex-drone-based-reforestation
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-DBR-PPO-baseline-task-6-difficulty-2
results:
- task:
type: sub-task
name: explore_furthest_distance_and_return_to_base
task-id: 6
difficulty-id: 2
dataset:
name: hivex-drone-based-reforestation
type: hivex-drone-based-reforestation
metrics:
- type: furthest_distance_explored
value: 168.98811096191406 +/- 14.397350162774785
name: Furthest Distance Explored
verified: true
- type: out_of_energy_count
value: 0.6052063649892807 +/- 0.07042549223909607
name: Out of Energy Count
verified: true
- type: recharge_energy_count
value: 105.80906302928925 +/- 107.6122695299055
name: Recharge Energy Count
verified: true
- type: cumulative_reward
value: 8.523429723531008 +/- 8.479993355695575
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 2 using the Proximal Policy Optimization (PPO) algorithm.
Environment: Drone-Based Reforestation
Task: 6
Difficulty: 2
Algorithm: PPO
Episode Length: 2000
Training max_steps: 1200000
Testing max_steps: 300000
Train & Test Scripts
Download the Environment