--- library_name: hivex original_train_name: DroneBasedReforestation_difficulty_7_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-7 results: - task: type: main-task name: main_task task-id: 0 difficulty-id: 7 dataset: name: hivex-drone-based-reforestation type: hivex-drone-based-reforestation metrics: - type: cumulative_distance_reward value: 3.159047532081604 +/- 0.8357707341134992 name: Cumulative Distance Reward verified: true - type: cumulative_distance_until_tree_drop value: 86.77309692382812 +/- 16.14500683977347 name: Cumulative Distance Until Tree Drop verified: true - type: cumulative_distance_to_existing_trees value: 47.3928982925415 +/- 9.37172991243217 name: Cumulative Distance to Existing Trees verified: true - type: cumulative_normalized_distance_until_tree_drop value: 0.3159047555923462 +/- 0.08357707265270713 name: Cumulative Normalized Distance Until Tree Drop verified: true - type: cumulative_tree_drop_reward value: 7.276955466270447 +/- 1.8673272306379547 name: Cumulative Tree Drop Reward verified: true - type: out_of_energy_count value: 0.9944761908054351 +/- 0.02380680114658389 name: Out of Energy Count verified: true - type: recharge_energy_count value: 9.957492036819458 +/- 0.8696697050846641 name: Recharge Energy Count verified: true - type: tree_drop_count value: 0.9822539675235749 +/- 0.03421961796573733 name: Tree Drop Count verified: true - type: cumulative_reward value: 11.030714321136475 +/- 2.7815221104620416 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 7 using the Proximal Policy Optimization (PPO) algorithm.

Environment: **Drone-Based Reforestation**
Task: 0
Difficulty: 7
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)