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--- |
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library_name: hivex |
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original_train_name: DroneBasedReforestation_difficulty_5_task_1_run_id_1_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-1-difficulty-5 |
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results: |
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- task: |
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type: sub-task |
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name: find_closest_forest_perimeter |
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task-id: 1 |
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difficulty-id: 5 |
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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: out_of_energy_count |
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value: 0.013677190858870744 +/- 0.015857580403509562 |
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name: Out of Energy Count |
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verified: true |
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- type: cumulative_reward |
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value: 98.19454025268554 +/- 2.013315524136616 |
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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>1</code> with difficulty <code>5</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>Environment: **Drone-Based Reforestation**<br>Task: <code>1</code><br>Difficulty: <code>5</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) |
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[hivex-paper]: https://arxiv.org/abs/2501.04180 |
