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--- |
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library_name: hivex |
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original_train_name: DroneBasedReforestation_difficulty_1_task_4_run_id_0_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-4-difficulty-1 |
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results: |
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- task: |
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type: sub-task |
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name: find_highest_potential_seed_drop_location |
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task-id: 4 |
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difficulty-id: 1 |
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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: highest_potential_soild_found |
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value: 0.9788281238079071 +/- 0.032543043270812776 |
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name: Highest Potential Soild Found |
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verified: true |
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- type: out_of_energy_count |
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value: 0.6642222416400909 +/- 0.0670919881726494 |
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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: -0.01776815608205652 +/- 0.022001924051183442 |
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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>4</code> with difficulty <code>1</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>Environment: **Drone-Based Reforestation**<br>Task: <code>4</code><br>Difficulty: <code>1</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 |
