--- library_name: hivex original_train_name: DroneBasedReforestation_difficulty_2_task_3_run_id_2_train tags: - hivex - hivex-drone-based-reforestation - reinforcement-learning - multi-agent-reinforcement-learning model-index: - name: hivex-DBR-PPO-baseline-task-3-difficulty-2 results: - task: type: sub-task name: drop_seed task-id: 3 difficulty-id: 2 dataset: name: hivex-drone-based-reforestation type: hivex-drone-based-reforestation metrics: - type: cumulative_distance_reward value: 1.2730848133563994 +/- 0.31384063871152373 name: Cumulative Distance Reward verified: true - type: cumulative_distance_until_tree_drop value: 46.571126289367676 +/- 6.711804112230181 name: Cumulative Distance Until Tree Drop verified: true - type: cumulative_distance_to_existing_trees value: 62.378562469482425 +/- 4.8385232941913126 name: Cumulative Distance to Existing Trees verified: true - type: cumulative_normalized_distance_until_tree_drop value: 0.12730848103761672 +/- 0.03138406355454151 name: Cumulative Normalized Distance Until Tree Drop verified: true - type: cumulative_tree_drop_reward value: 4.010982251167297 +/- 0.6601700266326962 name: Cumulative Tree Drop Reward verified: true - type: out_of_energy_count value: 0.05754003098234534 +/- 0.03282736545941193 name: Out of Energy Count verified: true - type: recharge_energy_count value: 11.035588264465332 +/- 0.725159645414964 name: Recharge Energy Count verified: true - type: tree_drop_count value: 0.9222618734836578 +/- 0.04192513553044461 name: Tree Drop Count verified: true - type: cumulative_reward value: 98.53370529174805 +/- 5.198916602319761 name: Cumulative Reward verified: true --- This model serves as the baseline for the **Drone-Based Reforestation** environment, trained and tested on task 3 with difficulty 2 using the Proximal Policy Optimization (PPO) algorithm.

Environment: **Drone-Based Reforestation**
Task: 3
Difficulty: 2
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)