metadata
library_name: hivex
original_train_name: DroneBasedReforestation_difficulty_2_task_2_run_id_1_train
tags:
- hivex
- hivex-drone-based-reforestation
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-DBR-PPO-baseline-task-2-difficulty-2
results:
- task:
type: sub-task
name: pick_up_seed_at_base
task-id: 2
difficulty-id: 2
dataset:
name: hivex-drone-based-reforestation
type: hivex-drone-based-reforestation
metrics:
- type: out_of_energy_count
value: 0.6057777959108352 +/- 0.08043704946542643
name: Out of Energy Count
verified: true
- type: recharge_energy_count
value: 104.45834933944046 +/- 94.23022771417737
name: Recharge Energy Count
verified: true
- type: cumulative_reward
value: 10.116059012413025 +/- 6.8049686040470005
name: Cumulative Reward
verified: true
This model serves as the baseline for the Drone-Based Reforestation environment, trained and tested on task 2 with difficulty 2 using the Proximal Policy Optimization (PPO) algorithm.
Environment: Drone-Based Reforestation
Task: 2
Difficulty: 2
Algorithm: PPO
Episode Length: 2000
Training max_steps: 1200000
Testing max_steps: 300000
Train & Test Scripts
Download the Environment