--- library_name: hivex original_train_name: DroneBasedReforestation_difficulty_1_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-1 results: - task: type: main-task name: main_task task-id: 0 difficulty-id: 1 dataset: name: hivex-drone-based-reforestation type: hivex-drone-based-reforestation metrics: - type: cumulative_distance_reward value: 2.0864201402664184 +/- 0.6296944874797746 name: Cumulative Distance Reward verified: true - type: cumulative_distance_until_tree_drop value: 63.19091514587402 +/- 12.303839558575664 name: Cumulative Distance Until Tree Drop verified: true - type: cumulative_distance_to_existing_trees value: 61.76650863647461 +/- 13.908253887773586 name: Cumulative Distance to Existing Trees verified: true - type: cumulative_normalized_distance_until_tree_drop value: 0.20864201247692107 +/- 0.06296944883377423 name: Cumulative Normalized Distance Until Tree Drop verified: true - type: cumulative_tree_drop_reward value: 5.931592869758606 +/- 1.8518746378631161 name: Cumulative Tree Drop Reward verified: true - type: out_of_energy_count value: 0.9266984140872956 +/- 0.06184757754397895 name: Out of Energy Count verified: true - type: recharge_energy_count value: 10.601777839660645 +/- 1.2478378815502142 name: Recharge Energy Count verified: true - type: tree_drop_count value: 1.0418095350265504 +/- 0.08056789785926544 name: Tree Drop Count verified: true - type: cumulative_reward value: 8.961133165359497 +/- 2.7381643935331064 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 1 using the Proximal Policy Optimization (PPO) algorithm.

Environment: **Drone-Based Reforestation**
Task: 0
Difficulty: 1
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)