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---
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library_name: hivex
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original_train_name: DroneBasedReforestation_difficulty_2_task_6_run_id_2_train
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tags:
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- hivex
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- hivex-drone-based-reforestation
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- reinforcement-learning
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- multi-agent-reinforcement-learning
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model-index:
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- name: hivex-DBR-PPO-baseline-task-6-difficulty-2
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results:
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- task:
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type: sub-task
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name: explore_furthest_distance_and_return_to_base
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task-id: 6
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difficulty-id: 2
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dataset:
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name: hivex-drone-based-reforestation
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type: hivex-drone-based-reforestation
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metrics:
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- type: furthest_distance_explored
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value: 168.98811096191406 +/- 14.397350162774785
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name: Furthest Distance Explored
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verified: true
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- type: out_of_energy_count
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value: 0.6052063649892807 +/- 0.07042549223909607
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name: Out of Energy Count
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verified: true
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- type: recharge_energy_count
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value: 105.80906302928925 +/- 107.6122695299055
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name: Recharge Energy Count
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verified: true
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- type: cumulative_reward
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value: 8.523429723531008 +/- 8.479993355695575
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name: Cumulative Reward
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verified: true
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---
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This model serves as the baseline for the **Drone-Based Reforestation** environment, trained and tested on task <code>6</code> with difficulty <code>2</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>Environment: **Drone-Based Reforestation**<br>Task: <code>6</code><br>Difficulty: <code>2</code><br>Algorithm: <code>PPO</code><br>Episode Length: <code>2000</code><br>Training <code>max_steps</code>: <code>1200000</code><br>Testing <code>max_steps</code>: <code>300000</code><br><br>Train & Test [Scripts](https://github.com/hivex-research/hivex)<br>Download the [Environment](https://github.com/hivex-research/hivex-environments) |
